import numpy as np
import pandas as pd
import os
import glob
import seaborn as sns
import matplotlib
from collections import Counter
import sklearn as skl
import sklearn.cluster as skcluster
import scipy.stats

!conda list

# packages in environment at /Users/mkelly95/anaconda3:
#
# Name                    Version                   Build  Channel
_ipyw_jlab_nb_ext_conf    0.1.0            py36h2fc01ae_0  
alabaster                 0.7.10           py36h174008c_0  
anaconda                  5.1.0                    py36_2  
anaconda-client           1.6.9                    py36_0  
anaconda-navigator        1.7.0                    py36_0  
anaconda-project          0.8.2            py36h9ee5d53_0  
appnope                   0.1.0            py36hf537a9a_0  
appscript                 1.0.1            py36h9e71e49_1  
asn1crypto                0.24.0                   py36_0  
astroid                   1.6.1                    py36_0  
astropy                   2.0.3            py36h1de35cc_0  
attrs                     17.4.0                   py36_0  
babel                     2.5.3                    py36_0  
backports                 1.0              py36ha3c1827_1  
backports.shutil_get_terminal_size 1.0.0            py36hd7a2ee4_2  
beautifulsoup4            4.6.0            py36h72d3c9f_1  
biopython                 1.78             py36haf1e3a3_0  
bitarray                  0.8.1            py36h1de35cc_1  
bkcharts                  0.2              py36h073222e_0  
blas                      2.12                   openblas    conda-forge
blaze                     0.11.3           py36h02e7a37_0  
bleach                    2.1.2                    py36_0  
bokeh                     0.12.13          py36h2f9c1c0_0  
boto                      2.48.0           py36hdbc59ac_1  
bottleneck                1.2.1            py36hbd380ad_0  
bzip2                     1.0.6                hd86a083_4  
ca-certificates           2021.5.30            h033912b_0    conda-forge
certifi                   2021.5.30        py36h79c6626_0    conda-forge
cffi                      1.11.4           py36h342bebf_0  
chardet                   3.0.4            py36h96c241c_1  
click                     6.7              py36hec950be_0  
cloudpickle               0.5.2                    py36_1  
clyent                    1.2.2            py36hae3ad88_0  
colorama                  0.3.9            py36hd29a30c_0  
conda                     4.4.10                   py36_0  
conda-build               3.4.1                    py36_0  
conda-env                 2.6.0                h36134e3_0  
conda-verify              2.0.0            py36he837df3_0  
contextlib2               0.5.5            py36hd66e5e7_0  
cryptography              2.1.4            py36h842514c_0  
curl                      7.58.0               ha441bb4_0  
cycler                    0.10.0           py36hfc81398_0  
cython                    0.27.3           py36h6ecb376_0  
cytoolz                   0.9.0            py36h1de35cc_0  
dask                      0.16.1                   py36_0  
dask-core                 0.16.1                   py36_0  
datashape                 0.5.4            py36hfb22df8_0  
dbus                      1.12.2               h5243cc1_1  
decorator                 4.2.1                    py36_0  
distributed               1.20.2                   py36_0  
docutils                  0.14             py36hbfde631_0  
entrypoints               0.2.3            py36hd81d71f_2  
et_xmlfile                1.0.1            py36h1315bdc_0  
expat                     2.2.5                hb8e80ba_0  
fastcache                 1.0.2            py36h1de35cc_2  
filelock                  2.0.13           py36hf9de95b_0  
flask                     0.12.2           py36h5658096_0  
flask-cors                3.0.3            py36h7387b97_0  
freetype                  2.8                  h12048fb_1  
get_terminal_size         1.0.0                h7520d66_0  
gettext                   0.19.8.1             h15daf44_3  
gevent                    1.2.2            py36ha70b9d6_0  
glib                      2.53.6               h33f6a65_2  
glob2                     0.6              py36h94c9186_0  
gmp                       6.1.2                hb37e062_1  
gmpy2                     2.0.8            py36hf9c35bd_2  
greenlet                  0.4.12           py36hf09ba7b_0  
h5py                      2.7.1            py36h39cdac5_0  
hdf5                      1.10.1               ha036c08_1  
heapdict                  1.0.0                    py36_2  
html5lib                  1.0.1            py36h2f9c1c0_0  
icu                       58.2                 h4b95b61_1  
idna                      2.6              py36h8628d0a_1  
imageio                   2.2.0            py36h5e01289_0  
imagesize                 0.7.1            py36h3495948_0  
intel-openmp              2018.0.0             h8158457_8  
ipykernel                 4.8.0                    py36_0  
ipython                   6.2.1            py36h3dda519_1  
ipython_genutils          0.2.0            py36h241746c_0  
ipywidgets                7.1.1                    py36_0  
isort                     4.2.15           py36hceb2a01_0  
itsdangerous              0.24             py36h49fbb8d_1  
jbig                      2.1                  h4d881f8_0  
jdcal                     1.3              py36h1986823_0  
jedi                      0.11.1                   py36_0  
jinja2                    2.10             py36hd36f9c5_0  
joblib                    1.0.0              pyhd8ed1ab_0    conda-forge
jpeg                      9b                   he5867d9_2  
jsonschema                2.6.0            py36hb385e00_0  
jupyter                   1.0.0                    py36_4  
jupyter_client            5.2.2                    py36_0  
jupyter_console           5.2.0            py36hccf5b1c_1  
jupyter_core              4.4.0            py36h79cf704_0  
jupyterlab                0.31.5                   py36_0  
jupyterlab_launcher       0.10.2                   py36_0  
lazy-object-proxy         1.3.1            py36h2fbbe47_0  
libblas                   3.8.0               12_openblas    conda-forge
libcblas                  3.8.0               12_openblas    conda-forge
libcurl                   7.58.0               hf30b1f0_0  
libcxx                    12.0.1               habf9029_0    conda-forge
libcxxabi                 4.0.1                hebd6815_0  
libedit                   3.1                  hb4e282d_0  
libffi                    3.2.1                h475c297_4  
libgfortran               3.0.1                h93005f0_2  
libiconv                  1.15                 hdd342a3_7  
liblapack                 3.8.0               12_openblas    conda-forge
liblapacke                3.8.0               12_openblas    conda-forge
libopenblas               0.3.7                hd44dcd8_1    conda-forge
libpng                    1.6.34               he12f830_0  
libsodium                 1.0.15               hd9e47c5_0  
libssh2                   1.8.0                h322a93b_4  
libtiff                   4.0.9                h0dac147_0  
libxml2                   2.9.7                hab757c2_0  
libxslt                   1.1.32               hb819dd2_0  
llvm-openmp               11.0.1               h7c73e74_0    conda-forge
llvmlite                  0.21.0           py36hff739e7_0  
locket                    0.2.0            py36hca03003_1  
lxml                      4.1.1            py36hef8c89e_1  
lzo                       2.10                 h362108e_2  
markupsafe                1.0              py36h3a1e703_1  
matplotlib                2.1.2            py36h6d6146d_0  
mccabe                    0.6.1            py36hdaeb55d_0  
mistune                   0.8.3                    py36_0  
mkl                       2018.0.1             hfbd8650_4  
mkl-service               1.1.2            py36h7ea6df4_4  
mpc                       1.0.3                h7a72875_5  
mpfr                      3.1.5                h711e7fd_2  
mpmath                    1.0.0            py36hf1b8295_2  
msgpack-python            0.5.1            py36h04f5b5a_0  
multipledispatch          0.4.9            py36hc5f92b5_0  
navigator-updater         0.1.0            py36h7aee5fb_0  
nbconvert                 5.3.1            py36h810822e_0  
nbformat                  4.4.0            py36h827af21_0  
ncurses                   6.0                  hd04f020_2  
networkx                  2.1                      py36_0  
nltk                      3.2.5            py36h1190bce_0  
nose                      1.3.7            py36h73fae2b_2  
notebook                  5.4.0                    py36_0  
numba                     0.36.2          np114py36hc2f221f_0  
numexpr                   2.7.0            py36h86efe34_0    conda-forge
numpy                     1.14.6          py36hde6bac1_1201    conda-forge
numpydoc                  0.7.0            py36he54d08e_0  
odo                       0.5.1            py36hc1af34a_0  
olefile                   0.45.1                   py36_0  
openpyxl                  2.4.10                   py36_0  
openssl                   1.0.2u               h0b31af3_0    conda-forge
packaging                 16.8             py36he5e8135_0  
pandas                    0.22.0           py36h0a44026_0  
pandoc                    1.19.2.1             ha5e8f32_1  
pandocfilters             1.4.2            py36h3b0b094_1  
parso                     0.1.1            py36hc90e01c_0  
partd                     0.3.8            py36hf5c4cb8_0  
path.py                   10.5             py36h7bcb313_0  
pathlib2                  2.3.0            py36h877a6d8_0  
patsy                     0.5.0                    py36_0  
pcre                      8.41                 hfb6ab37_1  
pep8                      1.7.1                    py36_0  
pexpect                   4.3.1                    py36_0  
pickleshare               0.7.4            py36hf512f8e_0  
pillow                    5.0.0            py36hfcce615_0  
pip                       9.0.1            py36h1555ced_4  
pkginfo                   1.4.1            py36h25bf955_0  
pluggy                    0.6.0            py36hb1d0581_0  
ply                       3.10             py36h10e714e_0  
prompt_toolkit            1.0.15           py36haeda067_0  
psutil                    5.4.3            py36h1de35cc_0  
ptyprocess                0.5.2            py36he6521c3_0  
py                        1.5.2            py36ha69170d_0  
pycodestyle               2.3.1            py36h83e8646_0  
pycosat                   0.6.3            py36hee92d8f_0  
pycparser                 2.18             py36h724b2fc_1  
pycrypto                  2.6.1            py36h1de35cc_7  
pycurl                    7.43.0.1         py36hdbc3d79_0  
pyflakes                  1.6.0            py36hea45e83_0  
pygments                  2.2.0            py36h240cd3f_0  
pylint                    1.8.2                    py36_0  
pyodbc                    4.0.22           py36h0a44026_0  
pyopenssl                 17.5.0           py36h51e4350_0  
pyparsing                 2.2.0            py36hb281f35_0  
pyqt                      5.6.0            py36he5c6137_6  
pysocks                   1.6.7            py36hfa33cec_1  
pytables                  3.4.2            py36hfbd7ab0_2  
pytest                    3.3.2                    py36_0  
python                    3.6.4                hc167b69_1  
python-dateutil           2.6.1            py36h86d2abb_1  
python.app                2                py36h54569d5_7  
python_abi                3.6                     1_cp36m    conda-forge
pytz                      2017.3           py36hf0bf824_0  
pywavelets                0.5.2            py36h2710a04_0  
pyyaml                    3.12             py36h2ba1e63_1  
pyzmq                     16.0.3           py36he48b5ad_0  
qt                        5.6.2               h9975529_14  
qtawesome                 0.4.4            py36h468c6fb_0  
qtconsole                 4.3.1            py36hd96c0ff_0  
qtpy                      1.3.1            py36h16bb863_0  
readline                  7.0                  hc1231fa_4  
requests                  2.18.4           py36h4516966_1  
rope                      0.10.7           py36h68959ac_0  
ruamel_yaml               0.15.35          py36h1de35cc_1  
scikit-image              0.13.1           py36h1de35cc_1  
scikit-learn              0.23.1           py36hef903b7_0    conda-forge
scipy                     1.3.1            py36hab3da7d_2    conda-forge
seaborn                   0.8.1            py36h595ecd9_0  
send2trash                1.4.2                    py36_0  
setuptools                38.4.0                   py36_0  
simplegeneric             0.8.1                    py36_2  
singledispatch            3.4.0.3          py36hf20db9d_0  
sip                       4.18.1           py36h2824476_2  
six                       1.11.0           py36h0e22d5e_1  
snowballstemmer           1.2.1            py36h6c7b616_0  
sortedcollections         0.5.3            py36he9c3ed6_0  
sortedcontainers          1.5.9                    py36_0  
sphinx                    1.6.6                    py36_0  
sphinxcontrib             1.0              py36h9364dc8_1  
sphinxcontrib-websupport  1.0.1            py36h92f4a7a_1  
spyder                    3.2.6                    py36_0  
sqlalchemy                1.2.1            py36h1de35cc_0  
sqlite                    3.22.0               h3efe00b_0  
statsmodels               0.8.0            py36h9c68fc9_0  
sympy                     1.1.1            py36h7f3cf04_0  
tblib                     1.3.2            py36hda67792_0  
terminado                 0.8.1                    py36_1  
testpath                  0.3.1            py36h625a49b_0  
threadpoolctl             2.2.0              pyh8a188c0_0    conda-forge
tk                        8.6.7                h35a86e2_3  
toolz                     0.9.0                    py36_0  
tornado                   4.5.3                    py36_0  
traitlets                 4.3.2            py36h65bd3ce_0  
typing                    3.6.2            py36haa2d9ef_0  
unicodecsv                0.14.1           py36he531d66_0  
unixodbc                  2.3.4                h4cb4dde_1  
urllib3                   1.22             py36h68b9469_0  
wcwidth                   0.1.7            py36h8c6ec74_0  
webencodings              0.5.1            py36h3b9701d_1  
werkzeug                  0.14.1                   py36_0  
wheel                     0.30.0           py36h5eb2c71_1  
widgetsnbextension        3.1.0                    py36_0  
wrapt                     1.10.11          py36hc29e774_0  
xlrd                      1.1.0            py36h336f4a2_1  
xlsxwriter                1.0.2            py36h3736301_0  
xlwings                   0.11.5                   py36_0  
xlwt                      1.2.0            py36h5ad1178_0  
xz                        5.2.3                h0278029_2  
yaml                      0.1.7                hc338f04_2  
zeromq                    4.2.2                ha360ad0_2  
zict                      0.1.3            py36h71da714_0  
zlib                      1.2.11               hf3cbc9b_2

Read in Data

OVCAR_dt=pd.read_csv("DESEQ_Normalized_Output_dt_nonzero_genes.tsv", sep="\t",header = 0)
OVCAR_dt[:10]

Process the data to define the mean of every gene and quartile information

samplenames = []
for val in OVCAR_dt.columns:
    if val[0:3] == 'OVC':
        samplenames.append(val)
samplenames[:10]

['OVCAR3_H10_DORM_1_no_chro',
 'OVCAR3_F9_83_chr12',
 'OVCAR3_B4_24_chr11',
 'OVCAR3_C6_26_chr17',
 'OVCAR3_A9_6_chr3',
 'OVCAR3_D11_SCRAMBLE_2_no_chro',
 'OVCAR3_H6_63_chr2',
 'OVCAR3_F7_106_chr4',
 'OVCAR3_G7_100_chr9',
 'OVCAR3_H12_49_chr10']

OVCAR_nozero = np.array(OVCAR_dt)

genemean = []
for row in OVCAR_nozero[:,1:95]:
    means = np.mean(row)
    genemean.append(means)
OVCAR_dt['Gene_Mean'] = genemean

lowerthreshold_TQN = []
upperthreshold_TQN = []
for row in OVCAR_nozero[:,1:95]:
    lowq = np.percentile(row,25)
    upq = np.percentile(row,75)
    IQR = upq - lowq
    lowthresh = lowq - (1.5 * IQR)
    upperthresh = upq + (1.5 * IQR)
    lowerthreshold_TQN.append(lowthresh)
    upperthreshold_TQN.append(upperthresh)
    
    
OVCAR_dt['LowerThreshold'] = lowerthreshold_TQN
OVCAR_dt['UpperThreshold'] = upperthreshold_TQN

OVCAR_dt[:10]

Rank all genes by thier mean expression across the entire cohort; from highest to lowest expression (Rank 1 = highest expression)

OVCAR_dt['Rank'] = OVCAR_dt['Gene_Mean'].rank(method = 'average', ascending = False)

OVCAR_dt[:10]

ranknames = []
for val in samplenames:
    name = val + '_rank'
    ranknames.append(name)
    OVCAR_dt[name] = OVCAR_dt[val].rank(method = 'average', ascending = False)
OVCAR_dt[:10]

Determine the change in rank value for every gene in every sample

DevNames = []
Deviation_in_Rank = []
for val in ranknames:
    geneset = [val]
    newname = val+'_deviation_in_rank'
    DevNames.append(newname)
    for i in range(len(OVCAR_dt[val])):
        rank = OVCAR_dt[val][i]
        sample = val
        #median = DESEQ_dt['Median'][i]
        true_rank = OVCAR_dt['Rank'][i]
        gene = OVCAR_dt['Gene'][i]
        #genechrom = DESEQ_dt['Chromosomes'][i]
        rank_dif = true_rank - rank
        #OVCAR_dt[newname][i] = true_rank
        Deviation_in_Rank.append([sample, gene, rank_dif])

for val in DevNames:
    set_of_values = []
    for i in range(len(Deviation_in_Rank)):
        name = val.split('_')[:3]
        name2 = Deviation_in_Rank[i][0].split('_')[:3]
        vals = Deviation_in_Rank[i][2]
        if name == name2:
            set_of_values.append(vals)
    OVCAR_dt[val] = set_of_values
OVCAR_dt[:10]

Establish Negative Controls

ControlNames = ['OVCAR3_H10_DORM_1_no_chro_rank_deviation_in_rank','OVCAR3_G11_SCRAMBLE_1_no_chro_rank_deviation_in_rank']

Dev_values_control = []
maxes = []
mins = []
for name in ControlNames:
    namevals = []
    for i in range(len(OVCAR_dt[name])):
        Dev_values_control.append(OVCAR_dt[name][i])
        namevals.append(OVCAR_dt[name][i])
    maxes.append(np.max(namevals))
    mins.append(np.min(namevals))
Dev_values_control[:3]
Dev_values_only = []
maxes = []
mins = []
for name in DevNames:
    namevals = []
    for i in range(len(OVCAR_dt[name])):
        Dev_values_only.append(OVCAR_dt[name][i])
        namevals.append(OVCAR_dt[name][i])
    maxes.append(np.max(namevals))
    mins.append(np.min(namevals))
Dev_values_only[:3]

[-96.0, 414.0, -68.0]

I can now look at the overall genomic distribution of rank changes

Rank Change Distribution Plot¶

matplotlib.pyplot.figure(figsize=(10,10))
sns.distplot(Dev_values_only,axlabel= 'Rank Order Changes')
#matplotlib.pyplot.savefig('Rank_Order_Distribution.pdf')

<matplotlib.axes._subplots.AxesSubplot at 0x11d0b79b0>

FDR Function¶

#FDR function that accepts lengths of gene lists from normals and non-normals 
#need to do sample by sample, should be the median of the controls vs the total count 
def FDR_Calculation(normals,rest):
    firstset = np.median(normals)
    values = []
    boolvals = []
    combo_of_vals = []
    #when looking for threshold cutoffs 
    FDR_Threshold_Internal = .05
    for i in range(len(rest)):
        secondset = rest[i]
        value = firstset/secondset
        values.append(value)
        booleanvalue = value < FDR_Threshold_Internal
        boolvals.append(booleanvalue)
        combo_of_vals.append([value,booleanvalue])
            
        
        
    return(values)

def FDR_Calculation_Threshold(normals,rest,thresh):
    firstset = np.median(normals)
    values = []
    boolvals = []
    combo_of_vals = []
    #when looking for threshold cutoffs 
    FDR_Threshold_Internal = thresh
    for i in range(len(rest)):
        name = FDR_Threshold_Up[i][0]
        secondset = rest[i]
        if secondset == 0:
            value = 'NA'
            values.append(value)
            booleanvalue = 'NA'
            boolvals.append(booleanvalue)
            combo_of_vals.append([value,booleanvalue,name,i])
        elif secondset != 0:
            value = firstset/secondset
            values.append(value)
            booleanvalue = value < FDR_Threshold_Internal
            boolvals.append(booleanvalue)
            combo_of_vals.append([value,booleanvalue,name,i])
            
        
        
    return(combo_of_vals)

Next I can determine the list of genes for each super enhancer at a set FDR threshold, for example at 0.1, this means every sample may have a different 'change in rank' cutoff value but they all will meet the FDR 0.1 cutoff criteria.

I can iterate through changes in rank while maintaining a set FDR and keep all SEs and Genes that meet said threshold; I can then remove all duplicate rows based on SE name; keeping the last occurance (the smallest change in rank that met the FDR requirement). These cutoff values are vizualized below.

matplotlib.pyplot.figure(figsize=(10,10))
sns.distplot(Dev_values_only,axlabel= 'Rank Order Changes')
matplotlib.pyplot.axvline(x=-2190, color='black')
matplotlib.pyplot.axvline(x=-2140, color='purple')
matplotlib.pyplot.axvline(x=-2100, color='blue')
matplotlib.pyplot.axvline(x=-2050, color='teal')
matplotlib.pyplot.axvline(x=-2000, color='green')
matplotlib.pyplot.axvline(x=-1950, color='cyan')
matplotlib.pyplot.axvline(x=-1900, color='yellow')
matplotlib.pyplot.axvline(x=-1800, color='red')


#we can zoom in using this 
matplotlib.pyplot.figure(figsize=(10,10))
sns.distplot(Dev_values_only,axlabel= 'Rank Order Changes')
matplotlib.pyplot.xlim(-2500,0)
matplotlib.pyplot.axvline(x=-2190, color='black')
matplotlib.pyplot.axvline(x=-2140, color='purple')
matplotlib.pyplot.axvline(x=-2100, color='blue')
matplotlib.pyplot.axvline(x=-2050, color='teal')
matplotlib.pyplot.axvline(x=-2000, color='green')
matplotlib.pyplot.axvline(x=-1950, color='cyan')
matplotlib.pyplot.axvline(x=-1900, color='yellow')
matplotlib.pyplot.axvline(x=-1800, color='red')

<matplotlib.lines.Line2D at 0x122962fd0>

Downregulated FDR cutoff analysis¶

This iterates through various changes in rank and keeps genes that pass the FDR of 0.1 for each SE

Overall_FDR_Passed_Sets = []
for thresh in [-2190,-2140,-2100,-2050,-2000,-1950,-1900,-1800]:
    FDR_Threshold_Up = []
    for val in DevNames:
        geneset = [val]
        #chromose = val.split('_')[-1]
        for i in range(len(OVCAR_dt[val])):
            value = OVCAR_dt[val][i]
            #median = DESEQ_dt['Median'][i]
            #set threshold 
            Threshold = thresh
            gene = OVCAR_dt['Gene'][i]
            #genechrom = DESEQ_dt['Chromosomes'][i]
            if value < Threshold :
                geneset.append(gene)
        FDR_Threshold_Up.append(geneset)

    norms = []
    non_norms = []
    for i in range(len(FDR_Threshold_Up)):
        lengths = len(FDR_Threshold_Up[i])-1
        if i == 0 or i == 67:
            norms.append(lengths)
            non_norms.append(lengths)
        else:
            non_norms.append(lengths)
    print(np.sum(norms),np.sum(non_norms))

    FDR_RES = FDR_Calculation_Threshold(norms,non_norms,.1)
    FDR_Passed_Set = []
    for i in range(len(FDR_RES)):
        boolval = FDR_RES[i][1]
        if boolval == True:
            FDR_Passed_Set.append(FDR_Threshold_Up[i])
    #FDR_Passed_Set_dt = pd.DataFrame(FDR_Passed_Set)
    
    for i in range(len(FDR_Passed_Set)):
        Overall_FDR_Passed_Sets.append(FDR_Passed_Set[i])

0 554
2 673
4 768
4 919
9 1137
13 1355
17 1634
25 2357

I can then save these outputs in a few different variables and look at them

Overall_FDR_Passed_Sets_dt = pd.DataFrame(Overall_FDR_Passed_Sets)
Overall_FDR_Passed_Sets_dt[:10]

Overall_FDR_Passed_Sets_dt=Overall_FDR_Passed_Sets_dt.drop_duplicates(subset=0, keep = 'last')
Overall_FDR_Passed_Sets_dt=Overall_FDR_Passed_Sets_dt.reset_index(drop='True')
Overall_FDR_Passed_Sets_dt[:10]

OVT = np.array(Overall_FDR_Passed_Sets_dt)
OVTT = []
for row in OVT:
    res = list(filter(None, row))
    OVTT.append(res)
OVTT[:10]

[['OVCAR3_B4_24_chr11_rank_deviation_in_rank', 'CALCA', 'P2RY2'],
 ['OVCAR3_C6_26_chr17_rank_deviation_in_rank', 'FERMT1', 'NDE1'],
 ['OVCAR3_F7_106_chr4_rank_deviation_in_rank',
  'CA8',
  'CCL20',
  'LTB',
  'PRKCH',
  'SERPINB7'],
 ['OVCAR3_G7_100_chr9_rank_deviation_in_rank', 'MIR22HG', 'RGS9', 'TMEM42'],
 ['OVCAR3_H12_49_chr10_rank_deviation_in_rank', 'RNF180'],
 ['OVCAR3_D6_96_chr3_rank_deviation_in_rank',
  'GALC',
  'LINC01508',
  'PAQR8',
  'POMT1',
  'PSTK',
  'SAA2'],
 ['OVCAR3_F5_FOX_ENH_chr3_rank_deviation_in_rank',
  'CXCL2',
  'HS6ST2',
  'IQANK1'],
 ['OVCAR3_G4_92_chr15_rank_deviation_in_rank', 'GLCCI1', 'NMNAT1'],
 ['OVCAR3_G8_23_chr20_rank_deviation_in_rank', 'CCDC149'],
 ['OVCAR3_B11_65_chr5_rank_deviation_in_rank', 'ANGPTL2', 'TMEM100']]

I ran this to save the downregulated genes as an output file for later needs

#Overall_FDR_Passed_Sets_dt.to_csv('OVCAR3_CRISPRI_SCREEN_FDR0.1_SEs_and_Genes.tsv', sep='\t', index= False)

I then repeat this for the upranked genes

matplotlib.pyplot.figure(figsize=(10,10))
sns.distplot(Dev_values_only,axlabel= 'Rank Order Changes')
matplotlib.pyplot.axvline(x=2190, color='black')
matplotlib.pyplot.axvline(x=2140, color='purple')
matplotlib.pyplot.axvline(x=2100, color='blue')
matplotlib.pyplot.axvline(x=2050, color='teal')
matplotlib.pyplot.axvline(x=2000, color='green')
matplotlib.pyplot.axvline(x=1950, color='cyan')
matplotlib.pyplot.axvline(x=1900, color='yellow')


#we can zoom in using this 
matplotlib.pyplot.figure(figsize=(10,10))
sns.distplot(Dev_values_only,axlabel= 'Rank Order Changes')
matplotlib.pyplot.xlim(0,2500)
matplotlib.pyplot.axvline(x=2190, color='black')
matplotlib.pyplot.axvline(x=2140, color='purple')
matplotlib.pyplot.axvline(x=2100, color='blue')
matplotlib.pyplot.axvline(x=2050, color='teal')
matplotlib.pyplot.axvline(x=2000, color='green')
matplotlib.pyplot.axvline(x=1950, color='cyan')
matplotlib.pyplot.axvline(x=1900, color='yellow')

<matplotlib.lines.Line2D at 0x12c622c50>

Upregulated FDR cutoff analysis¶

Overall_Up_FDR_Passed_Sets = []
for thresh in [2190,2140,2100,2050,2000,1950,1900]:
    FDR_Threshold_Up = []
    for val in DevNames:
        geneset = [val]
        #chromose = val.split('_')[-1]
        for i in range(len(OVCAR_dt[val])):
            value = OVCAR_dt[val][i]
            #median = DESEQ_dt['Median'][i]
            #set threshold 
            Threshold = thresh
            gene = OVCAR_dt['Gene'][i]
            #genechrom = DESEQ_dt['Chromosomes'][i]
            if value > Threshold :
                geneset.append(gene)
        FDR_Threshold_Up.append(geneset)

    norms = []
    non_norms = []
    for i in range(len(FDR_Threshold_Up)):
        lengths = len(FDR_Threshold_Up[i])-1
        if i == 0 or i == 67:
            norms.append(lengths)
            non_norms.append(lengths)
        else:
            non_norms.append(lengths)
    print(np.sum(norms),np.sum(non_norms))

    FDR_RES = FDR_Calculation_Threshold(norms,non_norms,.1)
    FDR_Passed_Set = []
    for i in range(len(FDR_RES)):
        boolval = FDR_RES[i][1]
        if boolval == True:
            FDR_Passed_Set.append(FDR_Threshold_Up[i])
    #FDR_Passed_Set_dt = pd.DataFrame(FDR_Passed_Set)
    
    for i in range(len(FDR_Passed_Set)):
        Overall_Up_FDR_Passed_Sets.append(FDR_Passed_Set[i])

0 326
2 398
2 477
2 589
4 734
5 904
5 1117

Overall_Up_FDR_Passed_Sets_dt = pd.DataFrame(Overall_Up_FDR_Passed_Sets)
Overall_Up_FDR_Passed_Sets_dt[:10]

Overall_Up_FDR_Passed_Sets_dt=Overall_Up_FDR_Passed_Sets_dt.drop_duplicates(subset=0, keep = 'last')
Overall_Up_FDR_Passed_Sets_dt=Overall_Up_FDR_Passed_Sets_dt.reset_index(drop='True')
Overall_Up_FDR_Passed_Sets_dt

#Overall_Up_FDR_Passed_Sets_dt.to_csv('OVCAR3_CRISPRI_SCREEN_FDR0.1_SEs_and_Genes_UP.tsv', sep='\t', index= False)

#upgenes =Overall_Up_FDR_Passed_Sets_dt[Overall_Up_FDR_Passed_Sets_dt[0] == 'OVCAR3_A3_60_chr20_rank_deviation_in_rank']
#upgenes

Investigating all FDR .1 Up and Down Rank SEs and Genes¶

First we create gene groups up and down

genesetforclust = []
for i in range(len(Overall_FDR_Passed_Sets)):
    for j in range(len(Overall_FDR_Passed_Sets[i])):
        if j > 0:
            genesetforclust.append(Overall_FDR_Passed_Sets[i][j])
genesetdownarray = np.unique(genesetforclust)

genesetforclustup = []
for i in range(len(Overall_Up_FDR_Passed_Sets)):
    for j in range(len(Overall_Up_FDR_Passed_Sets[i])):
        if j > 0:
            genesetforclustup.append(Overall_Up_FDR_Passed_Sets[i][j])
genesetuparray = np.unique(genesetforclustup)

nwsetsarrayupdown = np.concatenate((genesetdownarray, genesetuparray), axis=0)
nwsetsarrayupdown[:10]

array(['ABCC4', 'ABHD17C', 'AC006499.7', 'AC007996.1', 'AC008982.2',
       'AC010168.2', 'AC016717.2', 'AC018521.5', 'AC021078.1',
       'AC024940.1'], dtype='<U11')

Make a DT for all genes that are in the updown group

NEWDT = OVCAR_dt.loc[OVCAR_dt['Gene'].isin(nwsetsarrayupdown)]

rnkanmescols = OVCAR_dt.columns[95:]
rnkanmescols[:10]

Index(['Chromosomes', 'Gene_Mean', 'LowerThreshold', 'UpperThreshold', 'Rank',
       'OVCAR3_H10_DORM_1_no_chro_rank', 'OVCAR3_F9_83_chr12_rank',
       'OVCAR3_B4_24_chr11_rank', 'OVCAR3_C6_26_chr17_rank',
       'OVCAR3_A9_6_chr3_rank'],
      dtype='object')

rankcolnames = []
for row in OVCAR_dt.columns[1:194]:
    rankcolnames.append(row)

Retrieve all Ranks for the full dataset

Rank_DESEQ_DT = OVCAR_dt.drop(rankcolnames,axis=1)
Rank_DESEQ_DT[:10]

Rank_DESEQ_DT.shape

(12178, 95)

rank_cols_names_for_ind = Rank_DESEQ_DT.pivot_table(index='Gene').columns
rank_cols_names_for_ind[:10]

Index(['OVCAR3_A10_87_chr6_rank_deviation_in_rank',
       'OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank',
       'OVCAR3_A12_55_chr1_rank_deviation_in_rank',
       'OVCAR3_A1_17_chr17_rank_deviation_in_rank',
       'OVCAR3_A2_29_chr19_rank_deviation_in_rank',
       'OVCAR3_A3_60_chr20_rank_deviation_in_rank',
       'OVCAR3_A4_81_chr7_rank_deviation_in_rank',
       'OVCAR3_A5_21_chr20_rank_deviation_in_rank',
       'OVCAR3_A6_39_chr10_rank_deviation_in_rank',
       'OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank'],
      dtype='object')

#Take only significant genes and rank changes

This will make the DT just for the significant regions from the analysis

NEWDT_Rank = Rank_DESEQ_DT.loc[Rank_DESEQ_DT['Gene'].isin(nwsetsarrayupdown)]
NEWDT_Rank=NEWDT_Rank.reset_index(drop='True')
NEWDT_Rank.shape

(950, 95)

#NEWDT_Rank.to_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes.tsv', sep='\t', index= False)

If reading in this will make the same DT

#NEWDT_Rank = pd.DataFrame.from_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes.tsv', sep = '\t')

NEWDT_Rank.shape

(950, 95)

rank_cols_names_for_ind = NEWDT_Rank.pivot_table(index='Gene').columns
rank_cols_names_for_ind[:10]

Index(['OVCAR3_A10_87_chr6_rank_deviation_in_rank',
       'OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank',
       'OVCAR3_A12_55_chr1_rank_deviation_in_rank',
       'OVCAR3_A1_17_chr17_rank_deviation_in_rank',
       'OVCAR3_A2_29_chr19_rank_deviation_in_rank',
       'OVCAR3_A3_60_chr20_rank_deviation_in_rank',
       'OVCAR3_A4_81_chr7_rank_deviation_in_rank',
       'OVCAR3_A5_21_chr20_rank_deviation_in_rank',
       'OVCAR3_A6_39_chr10_rank_deviation_in_rank',
       'OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank'],
      dtype='object')

Full Datset of Genes¶

First I need to determine the min rank as the clustering algorithm requires non-negative numbers, I will adjust all changes in rank by this value, then use a scaler to shrink them. The clustering will be done on both rows and columns and the gene changes in rank will be Z scored. This clustering and heatmap will look at the entire dataset of Genes and SEs.

maxes_of_ranks = []
for i in range(len(NEWDT_Rank.pivot_table(index='Gene').columns)):
    curcol = rank_cols_names_for_ind[i]
    maxes_of_ranks.append(np.min(NEWDT_Rank.pivot_table(index='Gene')[curcol]))
np.min(maxes_of_ranks)

-4322.0

Rank_DESEQ_DT_Pivot=Rank_DESEQ_DT.pivot_table(index='Gene')

for i in range(len(Rank_DESEQ_DT.pivot_table(index='Gene').columns)):
    curcol = rank_cols_names_for_ind[i]
    for j in range(len(Rank_DESEQ_DT_Pivot['OVCAR3_A10_87_chr6_rank_deviation_in_rank'])):
                       Rank_DESEQ_DT_Pivot[curcol][j] = Rank_DESEQ_DT_Pivot[curcol][j]+4322

for i in range(len(Rank_DESEQ_DT.pivot_table(index='Gene').columns)):
    curcol = rank_cols_names_for_ind[i]
    for j in range(len(Rank_DESEQ_DT_Pivot['OVCAR3_A10_87_chr6_rank_deviation_in_rank'])):
                       Rank_DESEQ_DT_Pivot[curcol][j] = Rank_DESEQ_DT_Pivot[curcol][j]/1000

mapoclutrank =sns.clustermap(Rank_DESEQ_DT_Pivot,cmap='RdBu_r',robust=True,z_score=0, row_cluster=True,col_cluster=True,xticklabels = True,figsize=(20,10))
matplotlib.pyplot.xticks(rotation=30)
#matplotlib.pyplot.savefig('Rank_Heatmap_Wholeset.pdf', bbox_inches = 'tight')
matplotlib.pyplot.show()

FDR Detected SEs and Genes¶

First I need to determine the min rank as the clustering algorithm requires non-negative numbers, I will adjust all changes in rank by this value, then use a scaler to shrink them. The clustering will be done on both rows and columns and the gene changes in rank will be Z scored. This clustering and heatmap will look at a subset of Genes and SEs, those that passed the .1 FDR threshold for increase or decreased rank.

NEWDT_Pivot=NEWDT_Rank.pivot_table(index='Gene')

for i in range(len(NEWDT_Pivot.pivot_table(index='Gene').columns)):
    curcol = rank_cols_names_for_ind[i]
    for j in range(len(NEWDT_Pivot['OVCAR3_A10_87_chr6_rank_deviation_in_rank'])):
                       NEWDT_Pivot[curcol][j] = NEWDT_Pivot[curcol][j]+4322
            
for i in range(len(NEWDT_Pivot.pivot_table(index='Gene').columns)):
    curcol = rank_cols_names_for_ind[i]
    for j in range(len(NEWDT_Pivot['OVCAR3_A10_87_chr6_rank_deviation_in_rank'])):
                       NEWDT_Pivot[curcol][j] = NEWDT_Pivot[curcol][j]/1000
NEWDT_Pivot[:10]

zscoredata=scipy.stats.zscore(NEWDT_Pivot,axis=0)
zscoredata

array([[-0.13384429, -0.01881789, -0.99259729, ...,  0.37644394,
         0.83418006, -0.20158396],
       [ 0.81511892,  0.1347799 ,  0.80765218, ...,  0.83378201,
         0.81732779, -0.01384138],
       [ 1.90800073,  1.81988178,  0.4753666 , ...,  2.00955661,
         1.48804797, -0.01530812],
       ...,
       [ 1.86752363, -0.29618864,  0.36460473, ..., -0.21560098,
        -1.64478823,  1.2988899 ],
       [-0.83844573, -0.64066522,  0.10911404, ..., -0.18899222,
        -0.55276141, -0.28812155],
       [ 0.21096067,  0.60899441,  1.1414146 , ...,  0.65084678,
         0.8493471 ,  0.98354104]])

zdf = pd.DataFrame(zscoredata)
zdf.columns = NEWDT_Pivot.columns
zdf.index = NEWDT_Pivot.index
zdf

Ks = range(1, 10)
km = [skcluster.KMeans(n_clusters=i) for i in Ks]
score = [km[i].fit(zdf).score(zdf) for i in range(len(km))]

matplotlib.pyplot.plot(Ks,score)
#matplotlib.pyplot.savefig('Kmeans_Elbow_plot.pdf')

[<matplotlib.lines.Line2D at 0x12b6fa278>]

#km=skcluster.KMeans(n_clusters=3).fit(NEWDT_Pivot)
#cluster_map_df = pd.DataFrame()
#cluster_map_df['data_index'] = NEWDT_Pivot.index.values
#cluster_map_df['cluster'] = km.labels_

N = 3
km=skcluster.KMeans(n_clusters=N,n_init=50,max_iter=500,random_state=0).fit(zdf)
cluster_map_df = pd.DataFrame()
cluster_map_df['data_index'] = zdf.index.values
cluster_map_df['cluster'] = km.labels_
for i in range(0,N):
    idx=cluster_map_df['data_index'][cluster_map_df.cluster == i]
    idxgenes = []
    for row in idx:
        idxgenes.append(row)
    idxgenes[:10]
    Cluster0=zdf.loc[zdf.index.isin(idxgenes)]
    print(np.mean(Cluster0['OVCAR3_A3_60_chr20_rank_deviation_in_rank']),len(idxgenes))

0.9464559297682587 187
-0.9589149020511649 306
0.2547936568074226 457

km=skcluster.KMeans(n_clusters=N,n_init=50,max_iter=500,random_state=0).fit(zdf)
cluster_map_df = pd.DataFrame()
cluster_map_df['data_index'] = zdf.index.values
cluster_map_df['cluster'] = km.labels_

idx=cluster_map_df['data_index'][cluster_map_df.cluster == 0]
idxgenes = []
for row in idx:
    idxgenes.append(row)
idxgenes[:10]
Cluster0=zdf.loc[zdf.index.isin(idxgenes)]
print(np.mean(Cluster0['OVCAR3_A3_60_chr20_rank_deviation_in_rank']))

idx=cluster_map_df['data_index'][cluster_map_df.cluster == 1]
idxgenes = []
for row in idx:
    idxgenes.append(row)
idxgenes[:10]
Cluster1=zdf.loc[zdf.index.isin(idxgenes)]
print(np.mean(Cluster1['OVCAR3_A3_60_chr20_rank_deviation_in_rank']))

idx=cluster_map_df['data_index'][cluster_map_df.cluster == 2]
idxgenes = []
for row in idx:
    idxgenes.append(row)
idxgenes[:10]
Cluster2=zdf.loc[zdf.index.isin(idxgenes)]
print(np.mean(Cluster2['OVCAR3_A3_60_chr20_rank_deviation_in_rank']))

0.2547936568074226
-0.9589149020511649
0.9464559297682587

Cluster0df=pd.DataFrame(Cluster0)
Cluster1df=pd.DataFrame(Cluster1)
Cluster2df=pd.DataFrame(Cluster2)

#Cluster0df.to_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster0_mix60.tsv', sep='\t', index= True)
#Cluster1df.to_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster1_down60.tsv', sep='\t', index= True)
#Cluster2df.to_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster2_up60.tsv', sep='\t', index= True)

#read in existing data 
#Cluster0df = pd.read_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster0_mix60.tsv', sep='\t')
#Cluster0df = Cluster0df.pivot_table(index='Gene')
#Cluster1df = pd.read_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster1_down60.tsv', sep ='\t')
#Cluster1df = Cluster1df.pivot_table(index='Gene')
#Cluster2df = pd.read_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_Cluster2_up60.tsv', sep ='\t')
#Cluster2df = Cluster2df.pivot_table(index='Gene')

I want to plot down, up, then mixed clusters in that order

merged_df = pd.concat([Cluster1df, Cluster2df, Cluster0df])
merged_df[:10]

#merged_df.to_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_ZscoreRankChange_AllClust_downupmix60.tsv', sep='\t', index= True)

mapoclutrank =sns.clustermap(merged_df,cmap='RdBu_r',z_score=0,robust=True, method= 'average', metric='euclidean', row_cluster=False, col_cluster=True,xticklabels = True,figsize=(20,10))
matplotlib.pyplot.xticks(rotation=30)
#matplotlib.pyplot.savefig('Rank_Heatmap_RankDownUP_FDR.1Set.kmeans3clusters_version2.pdf', bbox_inches = 'tight')
matplotlib.pyplot.show()

#mapoclutrank.ax_heatmap.invert_xaxis()
#mapoclutrank.ax_col_dendrogram.invert_xaxis()
#mapoclutrank.fig

#this method gets scr1 and dorm1 near eachother, they are close via average/euclidean but the dendrogram plots from high to low, this also has identifiable clusters 
mapoclutrank =sns.clustermap(merged_df,cmap='RdBu_r',z_score=0,robust=True, method= 'complete', metric='correlation', row_cluster=False, col_cluster=True,xticklabels = True,figsize=(20,10))
matplotlib.pyplot.xticks(rotation=30)
#matplotlib.pyplot.savefig('Rank_Heatmap_RankDownUP_FDR.1Set.kmeans3clusters_complete_correlation.pdf', bbox_inches = 'tight')
matplotlib.pyplot.show()

Investigate General Patterns¶

I can now use this data to investigate general patterns and structures

No_dups_array = np.array(Overall_FDR_Passed_Sets_dt)
No_dups_array

array([['OVCAR3_B4_24_chr11_rank_deviation_in_rank', 'CALCA', 'P2RY2',
        ..., None, None, None],
       ['OVCAR3_C6_26_chr17_rank_deviation_in_rank', 'FERMT1', 'NDE1',
        ..., None, None, None],
       ['OVCAR3_F7_106_chr4_rank_deviation_in_rank', 'CA8', 'CCL20', ...,
        None, None, None],
       ...,
       ['OVCAR3_E11_12_chr19_rank_deviation_in_rank', 'ABCC4', 'ADRB2',
        ..., None, None, None],
       ['OVCAR3_F1_101_chr5_rank_deviation_in_rank', 'AC024940.1',
        'AC025154.2', ..., None, None, None],
       ['OVCAR3_A3_60_chr20_rank_deviation_in_rank', 'AC016717.2',
        'AC025449.1', ..., 'ZNF407', 'ZNF768', 'ZNF800']], dtype=object)

Cleaned_No_Dupe_Array = []
for i in range(len(No_dups_array)):
    filt=list(filter(None,No_dups_array[i]))
    Cleaned_No_Dupe_Array.append(filt)

#pdCNA= pd.DataFrame(Cleaned_No_Dupe_Array)
#pdCNA.to_csv('FDR.1_Screen_Results_Downgenes_everySE.tsv',sep='\t')

lengthsofsets = []
firsts = []
for i in range(len(Cleaned_No_Dupe_Array)):
    lengthsofsets.append(len(Cleaned_No_Dupe_Array[i]))
    firsts.append(Cleaned_No_Dupe_Array[i][0])

matplotlib.pyplot.figure(figsize=(25,25))


objects = firsts
y_pos = np.arange(len(objects))
performance = lengthsofsets
matplotlib.pyplot.barh(y_pos, performance, align='center', alpha=0.5)
matplotlib.pyplot.yticks(y_pos, objects)
matplotlib.pyplot.xlabel('Number of Genes Downranked')
matplotlib.pyplot.title('Number of Genes Downranked per SE')
#matplotlib.pyplot.savefig('Bar_Chart_Genes_Per_SE_FDR.1_DR.pdf', bbox_inches = 'tight')


matplotlib.pyplot.show()

namesdt = pd.DataFrame(firsts)
namesdt['nameofSE'] = firsts
namesdt['num_down_genes'] = performance
namesdt[:10]

Create a DF for plotting purposes with names and downregulated genes

sortred_names_dt=namesdt.sort_values('num_down_genes')
sortred_names_dt[:10]

matplotlib.pyplot.figure(figsize=(25,25))


objects = sortred_names_dt['nameofSE']
y_pos = np.arange(len(objects))
performance = sortred_names_dt['num_down_genes']
matplotlib.pyplot.barh(y_pos, performance, align='center', alpha=0.5)
matplotlib.pyplot.yticks(y_pos, objects)
matplotlib.pyplot.xlabel('Number of Genes Downranked')
matplotlib.pyplot.title('Number of Genes Downranked per SE')
#matplotlib.pyplot.savefig('Bar_Chart_Genes_Per_SE_FDR.1_DR_SORTED.pdf', bbox_inches = 'tight')


matplotlib.pyplot.show()

Read in the size information and construct the size and singnal dataframe

OVCAR_SE_SIZEDT=pd.read_csv("/Users/mkelly95/Documents/IGV/HG19/H3K27ac_Signal_over_BRD4_SuperEnhancers_TPM_1bpovlp.bed", sep="\t",header = None)
OVCAR_SE_SIZEDT[:10]

serankspots = []
for i in range(len(OVTT)):
    serankspots.append((str.split(OVTT[i][0],'_')[2],str.split(OVTT[i][0],'_')[3]))
serankspots[:10]

[('24', 'chr11'),
 ('26', 'chr17'),
 ('106', 'chr4'),
 ('100', 'chr9'),
 ('49', 'chr10'),
 ('96', 'chr3'),
 ('FOX', 'ENH'),
 ('92', 'chr15'),
 ('23', 'chr20'),
 ('65', 'chr5')]

sizesforchrscheck = []
for i in range(len(OVCAR_SE_SIZEDT[0])):
    size = OVCAR_SE_SIZEDT[2][i] - OVCAR_SE_SIZEDT[1][i]
    sizesforchrscheck.append(size)
sizesforchrscheck[:10]

[987, 2903, 19643, 32901, 2905, 29616, 9214, 7376, 20086, 24900]

stuffhelp = []
for row in serankspots:
    if len(row[0]) < 4 and row[0][0] != 'F':
        stuffhelp.append(int(row[0]))    
stuffhelp[:10]

[24, 26, 106, 100, 49, 96, 92, 23, 65, 94]

sizegrab = []
for row in stuffhelp:
    i = row-1
    sizegrab.append(sizesforchrscheck[i])
sizegrab[:10]

[10891, 72939, 25046, 18655, 24771, 14085, 37677, 30497, 22896, 22089]

plot_mat_names = []
plot_mat_lengths = []
plot_mat_numgenes = []
plot_mat_rank = []
for i in range(len(sortred_names_dt['nameofSE'])):
    name = sortred_names_dt['nameofSE'][i]
    numberofgenes = sortred_names_dt['num_down_genes'][i]
    splitname = str.split(name,'_')
    num = splitname[2]
    for j in range(len(stuffhelp)):
        if str(stuffhelp[j]) == num:
            plot_mat_names.append(name)
            plot_mat_lengths.append(int(sizegrab[j]))
            plot_mat_numgenes.append(int(numberofgenes))
            plot_mat_rank.append(stuffhelp[j])
            #print(name, numberofgenes, stuffhelp[j],sizegrab[j])
plottingdt = pd.DataFrame(plot_mat_names)
plottingdt['names'] = plot_mat_names
plottingdt['NumGenesDown'] = plot_mat_numgenes
plottingdt['SELength'] = plot_mat_lengths
plottingdt['SERank_H3K27ac'] = plot_mat_rank
plottingdt[:10]

matplotlib.pyplot.figure(figsize=(25,25))


objects = plottingdt['NumGenesDown']
y_pos = np.arange(len(objects))
performance = plottingdt['SERank_H3K27ac']
matplotlib.pyplot.scatter(performance, objects, s=150)
matplotlib.pyplot.xlabel('H3K27ac Rank of SE')
matplotlib.pyplot.ylabel('Number of Genes')
matplotlib.pyplot.title('H3K27ac Rank vs Counts')
#matplotlib.pyplot.savefig('SE_rank_vs_Genes_FDR.1.pdf', bbox_inches = 'tight')
matplotlib.pyplot.show()

matplotlib.pyplot.figure(figsize=(25,25))


objects = plottingdt['NumGenesDown']
y_pos = np.arange(len(objects))
performance = plottingdt['SELength']
matplotlib.pyplot.scatter(performance, objects, s=150)
matplotlib.pyplot.xlabel('Size of SE (bp)')
matplotlib.pyplot.ylabel('Number of Genes')
matplotlib.pyplot.title('SE Size vs Counts')
#matplotlib.pyplot.savefig('SE_Size_vs_Genes_FDR.1.pdf', bbox_inches = 'tight')
matplotlib.pyplot.show()

Fold Change¶

I now want to investigate the similarities between the Rank Change and Log Fold Change data; as we do not have replicate's we can't call DEGS with LFCs as we can't estimate the variation in the data. The list for DEGs will be the same as determined by the Rank Change analysis but the values will be LFCs, they will be calculated across the whole dataset similarly to how it was done with the rank changes. The starting dataset is a little different, instead of using the VST transformed DESEQ dataset I am starting with the raw counts data and performing the log2/fold change calculations on that.

#read in the data and remove the two outlier samples we earlier removed in the DESEQ normalization for rank based analysis
OVCAR_dt = pd.read_csv("OVCAR3_96_Well_Screen_LowExpressed_Genes_Removed.tsv", sep="\t",header = 0)
OVCAR_dt[:10]
OVCAR_dt=OVCAR_dt.drop(axis =1, columns='OVCAR3_H7_61')
OVCAR_dt=OVCAR_dt.drop(axis =1, columns='OVCAR3_F2_64')
samplenames = []
for val in OVCAR_dt.columns:
    if val[0:3] == 'OVC':
        samplenames.append(val)
samplenames[:10]

['OVCAR3_H10_DORM_1',
 'OVCAR3_F9_83',
 'OVCAR3_B4_24',
 'OVCAR3_C6_26',
 'OVCAR3_A9_6',
 'OVCAR3_D11_SCRAMBLE_2',
 'OVCAR3_H6_63',
 'OVCAR3_F7_106',
 'OVCAR3_G7_100',
 'OVCAR3_H12_49']

#add 1, convert to tpm
OVCAR2_dt=pd.read_csv("OVCAR3_96_Well_Screen_LowExpressed_Genes_Removed.tsv", sep="\t",header = 0)
OVCAR2_dt=OVCAR2_dt.drop(axis =1, columns='Gene')
OVCAR2_dt=OVCAR2_dt.drop(axis =1, columns='OVCAR3_H7_61')
OVCAR2_dt=OVCAR2_dt.drop(axis =1, columns='OVCAR3_F2_64')

OVCAR2_dt = OVCAR2_dt+1
for val in samplenames:
    OVCAR2_dt[val] = OVCAR2_dt[val]/(np.sum(OVCAR2_dt[val]/1000000))
#OVCAR2_dt = np.log2(OVCAR2_dt)
OVCAR2_dt[:10]

for val in samplenames:
    OVCAR_dt[val] = OVCAR2_dt[val]
OVCAR_dt[:10]

#dorm and scramble 
#samplenames[0]
#samplenames[67]

OVCAR_nozero = np.array(OVCAR_dt)

genemean = []
for row in OVCAR_nozero[:,1:]:
    means = np.median(row)
    genemean.append(means)
OVCAR_dt['Gene_Mean'] = genemean

#start after the mean is taken
#need raw counts at start 
OVCAR_dt[1:10]

DevNames = []
#Deviation_in_Rank =[]
Deviation_in_Fold = []
for val in samplenames:
    geneset = [val]
    newname = val+'_LFC'
    DevNames.append(newname)
    for i in range(len(OVCAR_dt[val])):
        rank = OVCAR_dt[val][i]
        sample = val
        #median = DESEQ_dt['Median'][i]
        true_rank = OVCAR_dt['Gene_Mean'][i]
        gene = OVCAR_dt['Gene'][i]
        dif = rank/true_rank
        #genechrom = DESEQ_dt['Chromosomes'][i]
        LFC_dif = np.log2(dif)
        #OVCAR_dt[newname][i] = true_rank
        Deviation_in_Fold.append([sample, gene, LFC_dif])

for val in DevNames:
    set_of_values = []
    for i in range(len(Deviation_in_Fold)):
        name = val.split('_')[:3]
        name2 = Deviation_in_Fold[i][0].split('_')[:3]
        vals = Deviation_in_Fold[i][2]
        if name == name2:
            set_of_values.append(vals)
    OVCAR_dt[val] = set_of_values
OVCAR_dt[:10]

LFC_only_dt=OVCAR_dt[OVCAR_dt.columns[96:]]
LFC_only_dt['Gene'] = OVCAR_dt['Gene']
LFC_only_dt[:10]

/Users/mkelly95/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

ControlNames = ['OVCAR3_H10_DORM_1_LFC','OVCAR3_G11_SCRAMBLE_1_LFC']

Dev_values_control = []
maxes = []
mins = []
for name in ControlNames:
    namevals = []
    for i in range(len(OVCAR_dt[name])):
        Dev_values_control.append(OVCAR_dt[name][i])
        namevals.append(OVCAR_dt[name][i])
    maxes.append(np.max(namevals))
    mins.append(np.min(namevals))
Dev_values_control[:3]
Dev_values_only = []
maxes = []
mins = []
for name in DevNames:
    namevals = []
    for i in range(len(OVCAR_dt[name])):
        Dev_values_only.append(OVCAR_dt[name][i])
        namevals.append(OVCAR_dt[name][i])
    maxes.append(np.max(namevals))
    mins.append(np.min(namevals))
Dev_values_only[:3]

[-0.2198827203347585, -1.0592580044519444, -0.07422144179446406]

GenevalsDT = LFC_only_dt.loc[LFC_only_dt['Gene'].isin(nwsetsarrayupdown)]
GenevalsDT=GenevalsDT.reset_index(drop='True')
GenevalsDT.shape

(950, 95)

JustLFCSdt=GenevalsDT
LFC_Pivot=JustLFCSdt.pivot_table(index='Gene')

Also make for Rank change

JustRanksdt=NEWDT_Rank[NEWDT_Rank.columns]
JR_Pivot=NEWDT_Rank.pivot_table(index='Gene')

Then I can calculate the correlation between the LFC/Rank Cahnge values for the genes in each super enhancer

names1 = JR_Pivot.columns
names2= LFC_Pivot.columns
Correlations_All_Cols = []
for i in range(len(names1)):
    corval = JR_Pivot[names1[i]].corr(LFC_Pivot[names2[i]])
    Correlations_All_Cols.append(corval)

This is the average correlation between LFC and RC

np.mean(Correlations_All_Cols)

0.7963383502057008

This is a plot of all correlation values, marked are the average (Blue), SE60 (black), and SE14 (Purple)

matplotlib.pyplot.scatter(x=Correlations_All_Cols, y=[1]*94, c=Correlations_All_Cols, cmap='Reds')
#SE60
matplotlib.pyplot.axvline(x=.8713, color='black')
#SE14
matplotlib.pyplot.axvline(x=.947, color='purple')
#AVG
matplotlib.pyplot.axvline(x=.79, color='blue')
matplotlib.pyplot.xlabel('Correlation Between LFC and Rank Change for Sig Genes')
#matplotlib.pyplot.xticks(ticks=Correlations_All_Cols, labels=LFC_only_dt.columns)
#matplotlib.pyplot.savefig('Correlation_of_all_sig_Genes_RC_vs_LFC_Allmarked_bluemed_black60_pur14.pdf', bbox_inches = 'tight')

Text(0.5,0,'Correlation Between LFC and Rank Change for Sig Genes')

I am next going to create a subset of this data where we just look at SE60's significant genes for plotting purposes

#for SE60 only plotting 
nwsetnwsetsarrayupdown = pd.read_csv('SE60_Genes_FDR.1_up_down_genes.txt',sep = '\n')

#forjustse60
NEWDT_Rank60 = NEWDT.loc[Rank_DESEQ_DT['Gene'].isin(nwsetnwsetsarrayupdown['OVCAR3_A3_60_chr20_rank_deviation_in_rank'])]
NEWDT_Rank60=NEWDT_Rank60.reset_index(drop='True')
NEWDT_Rank60.shape

GenevalsDT60 = GenevalsDT.loc[GenevalsDT['Gene'].isin(nwsetnwsetsarrayupdown['OVCAR3_A3_60_chr20_rank_deviation_in_rank'])]
GenevalsDT60=GenevalsDT60.reset_index(drop='True')
GenevalsDT60.shape

(196, 95)

JRC60 =NEWDT_Rank60.pivot_table(index='Gene')
LFCC60 = GenevalsDT60.pivot_table(index='Gene')

I can next plot, for every super-enhancer, or in this case SE60 then SE14 the Rank Change (X axis) vs the LFC (Y axis) for every gene determined as significant by the FDR .1 approach in any SE

#SE60
#all genes corr = 0.88 
#just SE genes sig corr = 0.94 
matplotlib.pyplot.scatter(x=JR_Pivot['OVCAR3_A3_60_chr20_rank_deviation_in_rank'],y=LFC_Pivot['OVCAR3_A3_60_LFC'])
#matplotlib.pyplot.savefig('Correlation_of_all_sig_Genes_RC_vs_LFC_SE60.pdf', bbox_inches = 'tight')

<matplotlib.collections.PathCollection at 0x12bcb54e0>

#SE14
matplotlib.pyplot.scatter(x=JR_Pivot['OVCAR3_D7_14_chr1_rank_deviation_in_rank'],y=LFC_Pivot['OVCAR3_D7_14_LFC'])
#matplotlib.pyplot.savefig('Correlation_of_all_sig_Genes_RC_vs_LFC_SE14.pdf', bbox_inches = 'tight')

<matplotlib.collections.PathCollection at 0x134175128>

This plot shows all FDR .1 genes from all samples in blue and the SE60 FDR genes in red, with the values being Rank Change (X axis) and LFC change (Y axis) for SE60

ax = matplotlib.pyplot.gca()


ax.scatter(x=JR_Pivot['OVCAR3_A3_60_chr20_rank_deviation_in_rank'],y=LFC_Pivot['OVCAR3_A3_60_LFC'], color="b")

ax.scatter(x=JRC60['OVCAR3_A3_60_chr20_rank_deviation_in_rank'],y=LFCC60['OVCAR3_A3_60_LFC'], color="r")

#matplotlib.pyplot.savefig('Correlation_of_sig_Genes_RC_vs_LFC_SE60_allgenesblue_se60onlyred.pdf', bbox_inches = 'tight')

<matplotlib.collections.PathCollection at 0x13412c320>

#read it in 
#GenevalsDT = pd.read_csv('OVCAR3_CRISPRI_SCREEN_DESEQ2_Normalized_FDR.1SigRankChangeGenes_LFC_Values_95cols_medianallsamples.tsv', sep ='\t')

combodf = pd.concat([JR_Pivot,LFC_Pivot],axis=1)
combodf[:10]

This html file contains the correlation of all RC to RC and LFC values, the main takeaway is that for every sample the highest correlation is between the RC and LFC values for that same SE.

corrtype = combodf.corr()
corrtype.style.background_gradient(cmap='coolwarm').set_precision(2)

htmlname =corrtype.style.background_gradient(cmap='coolwarm').set_precision(2).render()

#html_file = open("FDR.1GenesRCvsLFC_Corrs.html","w")
#html_file.write(htmlname)
#html_file.close()

from scipy.spatial import distance
from scipy.cluster import hierarchy

correlations = combodf.corr()
correlations_array = np.asarray(combodf.corr())

row_linkage = hierarchy.linkage(
    distance.pdist(correlations_array), method='average')

col_linkage = hierarchy.linkage(
    distance.pdist(correlations_array.T), method='average')

sns.clustermap(correlations, row_linkage=row_linkage, col_linkage=col_linkage, method="average", figsize=(20, 20))

<seaborn.matrix.ClusterGrid at 0x13413a860>

ranksonlynamesheatmap=[]
for name in correlations.columns:
    if 'rank_deviation_in_rank' in name:
        ranksonlynamesheatmap.append(name)

#correlations[94:][ranksonlynamesheatmap]

datplot=sns.clustermap(correlations[94:][ranksonlynamesheatmap],cmap='coolwarm', figsize=(20, 20))

datplot=sns.clustermap(correlations[94:][ranksonlynamesheatmap],row_cluster=False,col_cluster=False,cmap='coolwarm', figsize=(20, 20))
#matplotlib.pyplot.savefig('LFC_VS_RC_Corrplot_UnClustered.pdf')

	Gene	OVCAR3_H10_DORM_1_no_chro	OVCAR3_F9_83_chr12	OVCAR3_B4_24_chr11	OVCAR3_C6_26_chr17	OVCAR3_A9_6_chr3	OVCAR3_D11_SCRAMBLE_2_no_chro	OVCAR3_H6_63_chr2	OVCAR3_F7_106_chr4	OVCAR3_G7_100_chr9	...	OVCAR3_C11_16_chr17	OVCAR3_F4_47_chr12	OVCAR3_E5_90_chr12	OVCAR3_H1_RNF4_GENE_chr4	OVCAR3_C2_31_chr12	OVCAR3_D8_48_chr1	OVCAR3_E12_28_chr3	OVCAR3_B5_10_chr20	OVCAR3_A4_81_chr7	Chromosomes
0	A1BG-AS1	6.140697	6.328099	6.345255	6.170991	6.127679	6.067145	6.114298	6.161810	6.174310	...	6.681726	6.242046	6.395773	6.317477	6.352716	6.314677	6.188084	6.225434	6.353083	chr19
1	AAAS	7.184176	7.115772	7.058888	6.927719	6.935512	7.110241	7.242845	7.036975	7.116985	...	7.049252	7.049414	7.150416	6.979612	7.100470	7.162042	7.184400	7.117983	7.070964	chr12
2	AACS	8.094546	8.227321	8.103475	7.906169	7.826998	8.096278	8.126924	8.040755	8.141869	...	8.077216	8.110713	8.113601	8.289393	8.190960	7.996483	7.921800	8.275899	8.327605	chr12
3	AAGAB	9.128386	9.008106	9.266935	9.204514	9.141313	9.142991	9.171804	9.047017	9.168156	...	9.268539	9.522418	9.192757	9.265087	9.216418	9.212484	9.152172	9.279493	9.115985	chr15
4	AAK1	8.094035	8.121917	8.269935	8.319668	8.322567	8.340668	8.193842	7.997242	8.026352	...	8.358791	7.954043	8.002068	8.224111	8.256304	8.197615	7.999746	8.136464	8.180500	chr2
5	AAMDC	8.456480	8.650236	8.609379	8.621701	8.630103	8.693020	8.699156	8.613800	8.531603	...	8.442992	8.899980	8.871209	8.651503	8.799500	8.551512	8.643541	8.565462	8.618524	chr11
6	AAMP	9.301940	9.471465	9.553700	9.608462	9.451881	9.441904	9.647490	9.407065	9.433313	...	9.497448	9.783652	9.497128	9.304920	9.811345	9.512898	9.614655	9.559882	9.547459	chr2
7	AAR2	7.727690	7.829020	7.957309	7.861405	7.905020	7.871284	7.977899	8.076547	7.815893	...	7.818679	8.067131	8.068937	7.854773	7.926505	7.918243	7.966175	7.878828	7.671653	chr20
8	AARD	7.465548	7.297069	7.741117	7.413429	7.640225	7.412277	7.185343	7.293307	7.464068	...	7.436464	7.250057	7.503872	7.162015	7.053657	7.694022	7.347604	7.651043	7.616058	chr8
9	AARS	8.080457	7.949793	8.214453	8.157861	8.128305	8.446790	8.078110	7.999956	7.880981	...	8.234411	7.811236	7.613525	7.722388	8.235072	8.253548	7.933021	7.996016	8.038494	chr16

	Gene	OVCAR3_H10_DORM_1_no_chro	OVCAR3_F9_83_chr12	OVCAR3_B4_24_chr11	OVCAR3_C6_26_chr17	OVCAR3_A9_6_chr3	OVCAR3_D11_SCRAMBLE_2_no_chro	OVCAR3_H6_63_chr2	OVCAR3_F7_106_chr4	OVCAR3_G7_100_chr9	...	OVCAR3_H1_RNF4_GENE_chr4	OVCAR3_C2_31_chr12	OVCAR3_D8_48_chr1	OVCAR3_E12_28_chr3	OVCAR3_B5_10_chr20	OVCAR3_A4_81_chr7	Chromosomes	Gene_Mean	LowerThreshold	UpperThreshold
0	A1BG-AS1	6.140697	6.328099	6.345255	6.170991	6.127679	6.067145	6.114298	6.161810	6.174310	...	6.317477	6.352716	6.314677	6.188084	6.225434	6.353083	chr19	6.260879	5.894794	6.611113
1	AAAS	7.184176	7.115772	7.058888	6.927719	6.935512	7.110241	7.242845	7.036975	7.116985	...	6.979612	7.100470	7.162042	7.184400	7.117983	7.070964	chr12	7.085514	6.790230	7.398936
2	AACS	8.094546	8.227321	8.103475	7.906169	7.826998	8.096278	8.126924	8.040755	8.141869	...	8.289393	8.190960	7.996483	7.921800	8.275899	8.327605	chr12	8.101570	7.841206	8.357041
3	AAGAB	9.128386	9.008106	9.266935	9.204514	9.141313	9.142991	9.171804	9.047017	9.168156	...	9.265087	9.216418	9.212484	9.152172	9.279493	9.115985	chr15	9.188203	8.909883	9.477351
4	AAK1	8.094035	8.121917	8.269935	8.319668	8.322567	8.340668	8.193842	7.997242	8.026352	...	8.224111	8.256304	8.197615	7.999746	8.136464	8.180500	chr2	8.146579	7.772331	8.512219
5	AAMDC	8.456480	8.650236	8.609379	8.621701	8.630103	8.693020	8.699156	8.613800	8.531603	...	8.651503	8.799500	8.551512	8.643541	8.565462	8.618524	chr11	8.661998	8.375623	8.930864
6	AAMP	9.301940	9.471465	9.553700	9.608462	9.451881	9.441904	9.647490	9.407065	9.433313	...	9.304920	9.811345	9.512898	9.614655	9.559882	9.547459	chr2	9.534854	9.202852	9.860787
7	AAR2	7.727690	7.829020	7.957309	7.861405	7.905020	7.871284	7.977899	8.076547	7.815893	...	7.854773	7.926505	7.918243	7.966175	7.878828	7.671653	chr20	7.923991	7.566965	8.264797
8	AARD	7.465548	7.297069	7.741117	7.413429	7.640225	7.412277	7.185343	7.293307	7.464068	...	7.162015	7.053657	7.694022	7.347604	7.651043	7.616058	chr8	7.353919	6.942803	7.740643
9	AARS	8.080457	7.949793	8.214453	8.157861	8.128305	8.446790	8.078110	7.999956	7.880981	...	7.722388	8.235072	8.253548	7.933021	7.996016	8.038494	chr16	7.976568	7.491095	8.458228

	Gene	OVCAR3_H10_DORM_1_no_chro	OVCAR3_F9_83_chr12	OVCAR3_B4_24_chr11	OVCAR3_C6_26_chr17	OVCAR3_A9_6_chr3	OVCAR3_D11_SCRAMBLE_2_no_chro	OVCAR3_H6_63_chr2	OVCAR3_F7_106_chr4	OVCAR3_G7_100_chr9	...	OVCAR3_C2_31_chr12	OVCAR3_D8_48_chr1	OVCAR3_E12_28_chr3	OVCAR3_B5_10_chr20	OVCAR3_A4_81_chr7	Chromosomes	Gene_Mean	LowerThreshold	UpperThreshold	Rank
0	A1BG-AS1	6.140697	6.328099	6.345255	6.170991	6.127679	6.067145	6.114298	6.161810	6.174310	...	6.352716	6.314677	6.188084	6.225434	6.353083	chr19	6.260879	5.894794	6.611113	11967.0
1	AAAS	7.184176	7.115772	7.058888	6.927719	6.935512	7.110241	7.242845	7.036975	7.116985	...	7.100470	7.162042	7.184400	7.117983	7.070964	chr12	7.085514	6.790230	7.398936	7782.0
2	AACS	8.094546	8.227321	8.103475	7.906169	7.826998	8.096278	8.126924	8.040755	8.141869	...	8.190960	7.996483	7.921800	8.275899	8.327605	chr12	8.101570	7.841206	8.357041	3753.0
3	AAGAB	9.128386	9.008106	9.266935	9.204514	9.141313	9.142991	9.171804	9.047017	9.168156	...	9.216418	9.212484	9.152172	9.279493	9.115985	chr15	9.188203	8.909883	9.477351	1419.0
4	AAK1	8.094035	8.121917	8.269935	8.319668	8.322567	8.340668	8.193842	7.997242	8.026352	...	8.256304	8.197615	7.999746	8.136464	8.180500	chr2	8.146579	7.772331	8.512219	3623.0
5	AAMDC	8.456480	8.650236	8.609379	8.621701	8.630103	8.693020	8.699156	8.613800	8.531603	...	8.799500	8.551512	8.643541	8.565462	8.618524	chr11	8.661998	8.375623	8.930864	2293.0
6	AAMP	9.301940	9.471465	9.553700	9.608462	9.451881	9.441904	9.647490	9.407065	9.433313	...	9.811345	9.512898	9.614655	9.559882	9.547459	chr2	9.534854	9.202852	9.860787	1008.0
7	AAR2	7.727690	7.829020	7.957309	7.861405	7.905020	7.871284	7.977899	8.076547	7.815893	...	7.926505	7.918243	7.966175	7.878828	7.671653	chr20	7.923991	7.566965	8.264797	4354.0
8	AARD	7.465548	7.297069	7.741117	7.413429	7.640225	7.412277	7.185343	7.293307	7.464068	...	7.053657	7.694022	7.347604	7.651043	7.616058	chr8	7.353919	6.942803	7.740643	6570.0
9	AARS	8.080457	7.949793	8.214453	8.157861	8.128305	8.446790	8.078110	7.999956	7.880981	...	8.235072	8.253548	7.933021	7.996016	8.038494	chr16	7.976568	7.491095	8.458228	4171.0

	Gene	OVCAR3_H10_DORM_1_no_chro	OVCAR3_F9_83_chr12	OVCAR3_B4_24_chr11	OVCAR3_C6_26_chr17	OVCAR3_A9_6_chr3	OVCAR3_D11_SCRAMBLE_2_no_chro	OVCAR3_H6_63_chr2	OVCAR3_F7_106_chr4	OVCAR3_G7_100_chr9	...	OVCAR3_D1_5_chr7_rank	OVCAR3_C11_16_chr17_rank	OVCAR3_F4_47_chr12_rank	OVCAR3_E5_90_chr12_rank	OVCAR3_H1_RNF4_GENE_chr4_rank	OVCAR3_C2_31_chr12_rank	OVCAR3_D8_48_chr1_rank	OVCAR3_E12_28_chr3_rank	OVCAR3_B5_10_chr20_rank	OVCAR3_A4_81_chr7_rank
0	A1BG-AS1	6.140697	6.328099	6.345255	6.170991	6.127679	6.067145	6.114298	6.161810	6.174310	...	11827.0	9830.0	11798.0	11127.0	11566.0	11515.0	11605.0	11954.0	11905.0	11509.0
1	AAAS	7.184176	7.115772	7.058888	6.927719	6.935512	7.110241	7.242845	7.036975	7.116985	...	7876.0	8079.0	7911.0	7436.0	8309.0	7772.0	7543.0	7319.0	7683.0	7920.0
2	AACS	8.094546	8.227321	8.103475	7.906169	7.826998	8.096278	8.126924	8.040755	8.141869	...	3388.0	3825.0	3770.0	3755.0	3259.0	3497.0	4121.0	4370.0	3235.0	3097.0
3	AAGAB	9.128386	9.008106	9.266935	9.204514	9.141313	9.142991	9.171804	9.047017	9.168156	...	1791.0	1290.0	1068.0	1458.0	1356.0	1345.0	1367.0	1485.0	1275.0	1525.0
4	AAK1	8.094035	8.121917	8.269935	8.319668	8.322567	8.340668	8.193842	7.997242	8.026352	...	2958.0	3040.0	4271.0	4151.0	3435.0	3289.0	3483.0	4098.0	3666.0	3544.0
5	AAMDC	8.456480	8.650236	8.609379	8.621701	8.630103	8.693020	8.699156	8.613800	8.531603	...	2497.0	2797.0	1927.0	1974.0	2372.0	2000.0	2521.0	2357.0	2505.0	2380.0
6	AAMP	9.301940	9.471465	9.553700	9.608462	9.451881	9.441904	9.647490	9.407065	9.433313	...	1077.0	1029.0	830.0	1083.0	1299.0	779.0	1012.0	930.0	961.0	997.0
7	AAR2	7.727690	7.829020	7.957309	7.861405	7.905020	7.871284	7.977899	8.076547	7.815893	...	4426.0	4752.0	3910.0	3906.0	4644.0	4342.0	4392.0	4209.0	4527.0	5301.0
8	AARD	7.465548	7.297069	7.741117	7.413429	7.640225	7.412277	7.185343	7.293307	7.464068	...	6403.0	6302.0	6968.0	5923.0	7432.0	8001.0	5252.0	6612.0	5356.0	5503.0
9	AARS	8.080457	7.949793	8.214453	8.157861	8.128305	8.446790	8.078110	7.999956	7.880981	...	4997.0	3363.0	4766.0	5498.0	5133.0	3355.0	3315.0	4323.0	4119.0	3980.0

	Gene	OVCAR3_H10_DORM_1_no_chro	OVCAR3_F9_83_chr12	OVCAR3_B4_24_chr11	OVCAR3_C6_26_chr17	OVCAR3_A9_6_chr3	OVCAR3_D11_SCRAMBLE_2_no_chro	OVCAR3_H6_63_chr2	OVCAR3_F7_106_chr4	OVCAR3_G7_100_chr9	...	OVCAR3_D1_5_chr7_rank_deviation_in_rank	OVCAR3_C11_16_chr17_rank_deviation_in_rank	OVCAR3_F4_47_chr12_rank_deviation_in_rank	OVCAR3_E5_90_chr12_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_C2_31_chr12_rank_deviation_in_rank	OVCAR3_D8_48_chr1_rank_deviation_in_rank	OVCAR3_E12_28_chr3_rank_deviation_in_rank	OVCAR3_B5_10_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank
0	A1BG-AS1	6.140697	6.328099	6.345255	6.170991	6.127679	6.067145	6.114298	6.161810	6.174310	...	140.0	2137.0	169.0	840.0	401.0	452.0	362.0	13.0	62.0	458.0
1	AAAS	7.184176	7.115772	7.058888	6.927719	6.935512	7.110241	7.242845	7.036975	7.116985	...	-94.0	-297.0	-129.0	346.0	-527.0	10.0	239.0	463.0	99.0	-138.0
2	AACS	8.094546	8.227321	8.103475	7.906169	7.826998	8.096278	8.126924	8.040755	8.141869	...	365.0	-72.0	-17.0	-2.0	494.0	256.0	-368.0	-617.0	518.0	656.0
3	AAGAB	9.128386	9.008106	9.266935	9.204514	9.141313	9.142991	9.171804	9.047017	9.168156	...	-372.0	129.0	351.0	-39.0	63.0	74.0	52.0	-66.0	144.0	-106.0
4	AAK1	8.094035	8.121917	8.269935	8.319668	8.322567	8.340668	8.193842	7.997242	8.026352	...	665.0	583.0	-648.0	-528.0	188.0	334.0	140.0	-475.0	-43.0	79.0
5	AAMDC	8.456480	8.650236	8.609379	8.621701	8.630103	8.693020	8.699156	8.613800	8.531603	...	-204.0	-504.0	366.0	319.0	-79.0	293.0	-228.0	-64.0	-212.0	-87.0
6	AAMP	9.301940	9.471465	9.553700	9.608462	9.451881	9.441904	9.647490	9.407065	9.433313	...	-69.0	-21.0	178.0	-75.0	-291.0	229.0	-4.0	78.0	47.0	11.0
7	AAR2	7.727690	7.829020	7.957309	7.861405	7.905020	7.871284	7.977899	8.076547	7.815893	...	-72.0	-398.0	444.0	448.0	-290.0	12.0	-38.0	145.0	-173.0	-947.0
8	AARD	7.465548	7.297069	7.741117	7.413429	7.640225	7.412277	7.185343	7.293307	7.464068	...	167.0	268.0	-398.0	647.0	-862.0	-1431.0	1318.0	-42.0	1214.0	1067.0
9	AARS	8.080457	7.949793	8.214453	8.157861	8.128305	8.446790	8.078110	7.999956	7.880981	...	-826.0	808.0	-595.0	-1327.0	-962.0	816.0	856.0	-152.0	52.0	191.0

	0	1	2	3	4	5	6	7	8	9	...	118	119	120	121	122	123	124	125	126	127
0	OVCAR3_B4_24_chr11_rank_deviation_in_rank	CALCA	P2RY2	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
1	OVCAR3_C6_26_chr17_rank_deviation_in_rank	FERMT1	NDE1	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
2	OVCAR3_A9_6_chr3_rank_deviation_in_rank	BCAS3	C5orf34	CCDC142	CCDC181	CYP1B1	GCNT3	NAGA	PDZK1IP1	RNU6-925P	...	None	None	None	None	None	None	None	None	None	None
3	OVCAR3_D11_SCRAMBLE_2_no_chro_rank_deviation_i...	AC103591.3	AP001505.1	C1orf109	CLK1	CYP1B1	FAM102A	FURIN	GATD3A	GLIPR1	...	None	None	None	None	None	None	None	None	None	None
4	OVCAR3_F7_106_chr4_rank_deviation_in_rank	CA8	CCL20	LTB	PRKCH	SERPINB7	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
5	OVCAR3_G7_100_chr9_rank_deviation_in_rank	MIR22HG	RGS9	TMEM42	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
6	OVCAR3_H12_49_chr10_rank_deviation_in_rank	RNF180	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
7	OVCAR3_D6_96_chr3_rank_deviation_in_rank	GALC	LINC01508	PAQR8	POMT1	PSTK	SAA2	None	None	None	...	None	None	None	None	None	None	None	None	None	None
8	OVCAR3_E1_PLAG1_GENE_chr8_rank_deviation_in_rank	AC010168.2	BCORL1	CFB	GDF15	MAML3	PHF21A	RASD1	RASGEF1B	SLC16A9	...	None	None	None	None	None	None	None	None	None	None
9	OVCAR3_F5_FOX_ENH_chr3_rank_deviation_in_rank	CXCL2	HS6ST2	IQANK1	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

	0	1	2	3	4	5	6	7	8	9	...	65	66	67	68	69	70	71	72	73	74
0	OVCAR3_C6_26_chr17_rank_deviation_in_rank	DUSP1	FOS	MTLN	PXN-AS1	RASD1	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
1	OVCAR3_A9_6_chr3_rank_deviation_in_rank	SLC26A7	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
2	OVCAR3_D11_SCRAMBLE_2_no_chro_rank_deviation_i...	ABHD4	CASTOR1	CFB	CGN	CXCL2	CYBRD1	EGR3	FAM131A	HLA-DRA	...	None	None	None	None	None	None	None	None	None	None
3	OVCAR3_F7_106_chr4_rank_deviation_in_rank	CELSR3	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
4	OVCAR3_G7_100_chr9_rank_deviation_in_rank	CCL20	CD69	FCHSD1	IFNL1	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
5	OVCAR3_H12_49_chr10_rank_deviation_in_rank	LINC02303	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
6	OVCAR3_D6_96_chr3_rank_deviation_in_rank	MSANTD2	SMG6	TRDMT1	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
7	OVCAR3_E1_PLAG1_GENE_chr8_rank_deviation_in_rank	CCK	PCDH9	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
8	OVCAR3_G4_92_chr15_rank_deviation_in_rank	IFNL1	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
9	OVCAR3_B11_65_chr5_rank_deviation_in_rank	CFB	NPAS2	PAQR8	PDZK1IP1	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

	Gene	OVCAR3_H10_DORM_1_no_chro_rank_deviation_in_rank	OVCAR3_F9_83_chr12_rank_deviation_in_rank	OVCAR3_B4_24_chr11_rank_deviation_in_rank	OVCAR3_C6_26_chr17_rank_deviation_in_rank	OVCAR3_A9_6_chr3_rank_deviation_in_rank	OVCAR3_D11_SCRAMBLE_2_no_chro_rank_deviation_in_rank	OVCAR3_H6_63_chr2_rank_deviation_in_rank	OVCAR3_F7_106_chr4_rank_deviation_in_rank	OVCAR3_G7_100_chr9_rank_deviation_in_rank	...	OVCAR3_D1_5_chr7_rank_deviation_in_rank	OVCAR3_C11_16_chr17_rank_deviation_in_rank	OVCAR3_F4_47_chr12_rank_deviation_in_rank	OVCAR3_E5_90_chr12_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_C2_31_chr12_rank_deviation_in_rank	OVCAR3_D8_48_chr1_rank_deviation_in_rank	OVCAR3_E12_28_chr3_rank_deviation_in_rank	OVCAR3_B5_10_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank
0	A1BG-AS1	-96.0	418.0	449.0	-82.0	-96.0	-141.0	-132.0	-54.0	-4.0	...	140.0	2137.0	169.0	840.0	401.0	452.0	362.0	13.0	62.0	458.0
1	AAAS	414.0	125.0	-192.0	-830.0	-792.0	12.0	713.0	-224.0	108.0	...	-94.0	-297.0	-129.0	346.0	-527.0	10.0	239.0	463.0	99.0	-138.0
2	AACS	-68.0	359.0	-12.0	-635.0	-998.0	-67.0	62.0	-245.0	66.0	...	365.0	-72.0	-17.0	-2.0	494.0	256.0	-368.0	-617.0	518.0	656.0
3	AAGAB	-87.0	-292.0	144.0	70.0	-42.0	-29.0	-46.0	-228.0	-54.0	...	-372.0	129.0	351.0	-39.0	63.0	74.0	52.0	-66.0	144.0	-106.0
4	AAK1	-201.0	-99.0	367.0	539.0	517.0	542.0	111.0	-518.0	-423.0	...	665.0	583.0	-648.0	-528.0	188.0	334.0	140.0	-475.0	-43.0	79.0
5	AAMDC	-496.0	-44.0	-98.0	-57.0	-40.0	57.0	50.0	-143.0	-331.0	...	-204.0	-504.0	366.0	319.0	-79.0	293.0	-228.0	-64.0	-212.0	-87.0
6	AAMP	-257.0	-68.0	30.0	91.0	-45.0	-70.0	95.0	-144.0	-130.0	...	-69.0	-21.0	178.0	-75.0	-291.0	229.0	-4.0	78.0	47.0	11.0
7	AAR2	-753.0	-364.0	124.0	-203.0	-84.0	-242.0	165.0	487.0	-452.0	...	-72.0	-398.0	444.0	448.0	-290.0	12.0	-38.0	145.0	-173.0	-947.0
8	AARD	447.0	-238.0	1524.0	252.0	1158.0	186.0	-753.0	-236.0	444.0	...	167.0	268.0	-398.0	647.0	-862.0	-1431.0	1318.0	-42.0	1214.0	1067.0
9	AARS	311.0	-115.0	749.0	608.0	505.0	1364.0	319.0	37.0	-410.0	...	-826.0	808.0	-595.0	-1327.0	-962.0	816.0	856.0	-152.0	52.0	191.0

	OVCAR3_A10_87_chr6_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A12_55_chr1_rank_deviation_in_rank	OVCAR3_A1_17_chr17_rank_deviation_in_rank	OVCAR3_A2_29_chr19_rank_deviation_in_rank	OVCAR3_A3_60_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank	OVCAR3_A5_21_chr20_rank_deviation_in_rank	OVCAR3_A6_39_chr10_rank_deviation_in_rank	OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	...	OVCAR3_H11_18_chr12_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_H2_79_chr3_rank_deviation_in_rank	OVCAR3_H3_43_chr11_rank_deviation_in_rank	OVCAR3_H4_86_chr5_rank_deviation_in_rank	OVCAR3_H5_88_chr4_rank_deviation_in_rank	OVCAR3_H6_63_chr2_rank_deviation_in_rank	OVCAR3_H8_36_chr14_rank_deviation_in_rank	OVCAR3_H9_25_chr22_rank_deviation_in_rank
Gene
ABCC4	4.230	4.293	3.634	3.326	4.731	4.501	5.021	5.368	4.199	3.348	...	4.586	4.913	4.030	4.184	3.558	4.783	3.691	4.584	4.781	4.198
ABHD17C	4.863	4.396	4.853	4.791	3.824	3.189	4.498	3.373	4.219	3.240	...	4.001	4.593	4.767	3.480	4.349	4.014	4.889	4.859	4.771	4.326
ABHD4	5.592	5.526	4.628	3.859	4.852	4.599	5.176	4.138	4.769	3.870	...	4.439	4.136	4.192	5.325	5.000	3.818	5.085	5.566	5.169	4.325
AC004870.2	4.302	4.230	4.353	5.347	6.532	5.753	5.887	4.067	5.662	5.411	...	4.702	4.242	4.602	4.437	4.339	4.115	4.190	4.035	4.232	4.320
AC004980.1	4.628	5.309	3.320	4.950	4.965	3.879	4.629	3.806	3.908	4.794	...	5.071	4.375	4.164	4.413	4.209	4.430	3.739	3.372	3.766	3.974
AC006499.7	6.129	4.877	3.906	3.571	4.765	3.214	4.333	4.583	4.483	4.566	...	3.409	3.625	3.596	4.517	5.453	4.378	4.878	5.155	3.894	3.226
AC007996.1	3.861	3.646	4.990	5.396	4.170	3.840	3.382	3.324	5.258	3.445	...	4.628	3.885	4.359	3.909	4.968	3.712	4.669	3.772	4.625	4.042
AC008736.1	4.626	3.715	3.553	4.039	4.234	4.821	3.790	4.112	4.351	4.957	...	3.784	3.796	3.482	3.566	3.760	3.024	4.237	4.064	3.938	4.122
AC008982.2	4.928	5.835	4.264	4.121	3.980	4.945	4.274	5.336	4.612	5.260	...	4.277	5.134	3.086	4.602	4.155	4.213	3.753	3.931	4.044	1.718
AC009404.1	3.821	4.541	4.986	4.161	3.785	4.929	4.516	6.295	4.067	5.542	...	4.070	3.868	4.411	4.152	3.824	4.740	4.743	4.595	4.180	5.290

	OVCAR3_A10_87_chr6_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A12_55_chr1_rank_deviation_in_rank	OVCAR3_A1_17_chr17_rank_deviation_in_rank	OVCAR3_A2_29_chr19_rank_deviation_in_rank	OVCAR3_A3_60_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank	OVCAR3_A5_21_chr20_rank_deviation_in_rank	OVCAR3_A6_39_chr10_rank_deviation_in_rank	OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	...	OVCAR3_H11_18_chr12_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_H2_79_chr3_rank_deviation_in_rank	OVCAR3_H3_43_chr11_rank_deviation_in_rank	OVCAR3_H4_86_chr5_rank_deviation_in_rank	OVCAR3_H5_88_chr4_rank_deviation_in_rank	OVCAR3_H6_63_chr2_rank_deviation_in_rank	OVCAR3_H8_36_chr14_rank_deviation_in_rank	OVCAR3_H9_25_chr22_rank_deviation_in_rank
Gene
ABCC4	-0.133844	-0.018818	-0.992597	-1.270215	0.485128	0.132996	0.820528	1.210554	-0.147090	-1.237578	...	0.503577	1.031456	-0.544975	-0.233895	-1.120418	0.680271	-1.023838	0.376444	0.834180	-0.201584
ABHD17C	0.815119	0.134780	0.807652	0.589892	-0.681925	-0.906161	0.138247	-1.313295	-0.117466	-1.374710	...	-0.515741	0.474941	0.742107	-1.364072	0.048105	-0.453863	0.869038	0.833782	0.817328	-0.013841
ABHD4	1.908001	1.819882	0.475367	-0.593466	0.640821	0.210616	1.022734	-0.345503	0.697197	-0.574774	...	0.247441	-0.319834	-0.262062	1.597827	1.009809	-0.742927	1.178724	2.009557	1.488048	-0.015308
AC004870.2	-0.025905	-0.112766	0.069240	1.295844	2.802506	1.124631	1.950271	-0.435325	2.019914	1.381896	...	0.705698	-0.135488	0.453954	0.172262	0.033332	-0.304907	-0.235403	-0.536569	-0.091009	-0.022642
AC004980.1	0.462818	1.496283	-1.456320	0.791774	0.786220	-0.359653	0.309143	-0.765512	-0.578121	0.598466	...	1.348652	0.095814	-0.310960	0.133734	-0.158713	0.159661	-0.947997	-1.639170	-0.876325	-0.530133
AC006499.7	2.713045	0.852067	-0.590901	-0.959139	0.528876	-0.886360	-0.077004	0.217461	0.273572	0.308965	...	-1.547255	-1.208520	-1.302904	0.300692	1.679014	0.082970	0.851658	1.326044	-0.660616	-1.627254
AC007996.1	-0.687031	-0.983651	1.009977	1.358060	-0.236721	-0.390543	-1.317634	-1.375284	1.421507	-1.114414	...	0.576759	-0.756351	0.029584	-0.675370	0.962536	-0.899258	0.521432	-0.973951	0.571285	-0.430395
AC008736.1	0.459820	-0.880755	-1.112220	-0.364921	-0.154371	0.386449	-0.785376	-0.378396	0.078053	0.805434	...	-0.893847	-0.911132	-1.501991	-1.226011	-0.822009	-1.913932	-0.161141	-0.488341	-0.586466	-0.313056
AC008982.2	0.912564	2.280675	-0.062198	-0.260805	-0.481197	0.484662	-0.153973	1.170071	0.464648	1.190165	...	-0.034832	1.415800	-2.193558	0.437148	-0.238486	-0.160375	-0.925876	-0.709526	-0.407832	-3.839096
AC009404.1	-0.746997	0.351010	1.004070	-0.210017	-0.732107	0.471990	0.161729	2.383290	-0.342609	1.548232	...	-0.395514	-0.785916	0.120395	-0.285267	-0.727463	0.616854	0.638354	0.394737	-0.178641	1.400095
AC010168.2	-2.504004	-1.686025	-1.937765	0.954296	0.217491	1.022458	-0.687535	-0.373335	1.245244	1.461889	...	-1.787710	-1.721558	-1.496752	-0.312558	-0.863372	-1.293034	-0.955897	-1.097016	0.716214	-1.325106
AC012020.2	-1.016845	-0.129170	0.157849	0.156925	-0.963716	0.443476	0.037796	2.994327	-1.305393	0.095649	...	0.994940	-0.112879	-0.122351	-0.553363	0.455832	-0.785697	-0.548249	-1.883638	0.488709	-1.404310
AC012510.1	-0.073878	0.561275	0.875586	-0.170657	-0.137644	-0.020660	-0.007863	-0.170921	0.418731	0.056287	...	-0.243923	0.224508	0.013866	-0.153627	0.238673	0.221603	0.782137	-0.278797	0.800476	1.185951
AC015912.3	-0.226792	0.719346	-0.968968	0.446416	0.034777	-0.657461	-0.330088	-1.295584	-1.105430	-0.030055	...	-1.981119	0.874936	0.962150	-0.845539	-1.452804	-1.698609	0.725256	1.129804	-0.920141	0.273639
AC016205.1	-0.850439	1.799004	-0.508199	-0.654412	2.052350	1.241853	-0.122664	-0.406228	0.734228	-0.069417	...	-0.087105	1.135803	-1.021737	0.377749	-1.581327	-0.394871	-1.485207	-0.902440	-1.715568	0.432047
AC016717.2	0.073039	0.483730	-1.351466	0.433719	-2.969709	-1.525537	-0.485329	0.150411	-0.453700	0.435939	...	0.477440	-1.335475	-1.704571	0.624975	-0.508827	1.728866	0.758436	0.268346	-0.060675	-1.061093
AC016866.1	1.597676	1.129437	0.255320	-0.116060	0.585492	0.009438	1.902002	-0.084895	-0.077473	0.358485	...	0.020926	0.264508	0.401563	1.767995	0.101286	0.245200	2.524910	-0.157394	0.436467	0.292707
AC018521.5	0.852598	-0.431892	-0.105026	0.009640	-0.191686	0.091810	-0.130491	-0.436590	-1.373528	0.254366	...	0.813728	-1.290258	-1.164940	-1.298252	-0.442350	-0.962675	0.674695	0.273335	1.223467	0.251638
AC019205.1	1.272360	0.248114	0.745626	0.159465	0.159588	-0.533902	0.786610	2.435159	0.980108	0.698775	...	1.139561	-0.163314	0.820694	0.161025	0.061400	-0.368324	1.916599	2.011220	1.868909	-1.021491
AC021078.1	0.333891	2.028655	-1.028041	-1.126739	-0.229001	-0.647164	-0.713626	1.447125	0.082497	0.494347	...	0.735319	0.323638	-0.094409	-0.217841	0.925605	0.100668	1.041262	-1.263321	0.534210	-1.992472
AC024940.1	-1.253711	-0.294697	0.284856	-1.233394	0.865996	-0.043629	-1.407648	-0.298695	0.439468	0.989546	...	0.160319	-0.175487	-0.916954	0.748589	-0.542804	0.172934	1.038102	1.166392	-0.463444	0.671126
AC025154.2	-0.663045	-1.821728	-0.745968	1.502805	2.044630	-0.211542	0.059974	-0.676956	0.679423	1.067000	...	-0.442559	-0.669395	-0.429714	-2.054379	1.863673	-2.042241	0.200685	-2.673585	-0.994291	-1.008290
AC025449.1	-1.649487	0.937067	-0.305874	0.122643	1.713943	-1.473263	-0.452716	-0.689607	-1.606078	0.637828	...	0.367668	1.427974	0.918491	-1.274172	0.730605	1.283471	-2.437965	-1.245027	-0.690950	-1.102162
AC027307.2	0.573755	0.689521	-1.366234	-0.095745	-1.515718	-0.883192	-0.945836	1.342123	0.972702	-0.049102	...	-1.087256	-0.095488	-0.590381	1.079294	-0.689054	0.835127	-0.251203	-0.230568	-0.706117	1.180084
AC090109.1	-1.073813	0.606012	-1.572989	-0.027181	-0.196833	0.439516	0.487867	0.195954	0.366888	-0.030055	...	-0.324074	-1.220694	-1.758709	-0.829485	-0.804282	-0.058612	-1.769612	-1.085375	-0.485352	-0.505199
AC093909.1	-0.597082	0.418116	0.757440	0.448956	0.800374	-0.374702	-0.365310	-0.559303	0.738671	-0.338602	...	1.991606	-0.123314	-0.462895	-0.807010	-0.188259	-0.094008	1.829697	-0.830929	0.315130	1.190351
AC103591.3	0.756652	-0.649613	-1.553791	-0.052575	0.478694	-0.655084	0.581795	-1.155159	0.347633	-0.450339	...	-0.332786	-0.069402	0.796244	0.514205	0.965491	0.180308	-1.401465	-0.164047	-0.689265	0.696060
AC103702.2	-1.553541	-0.850930	-1.011796	-0.936285	-0.514652	0.323878	0.784000	0.417344	-0.781046	0.173103	...	-1.202256	1.393192	-0.565932	0.091994	-1.167691	1.090270	-0.311244	-0.533243	-0.305033	1.241687
AC109326.1	-0.033401	-0.643648	1.355554	-0.894385	-0.267602	-0.270153	-0.270078	0.400898	-0.388527	-1.586757	...	-0.874680	-0.735481	-0.426221	-0.964336	-1.073145	-0.144152	-0.033159	-0.894124	-1.295946	-0.032909
AC113410.3	0.749156	1.405317	2.077722	0.071856	-0.460610	-0.380246	-0.691449	0.321198	-0.181158	-0.243372	...	0.034865	-0.812002	0.949926	1.779233	-0.192691	1.522391	-0.434487	-0.031003	0.692621	1.334092
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
ZNF34	0.031062	0.571713	0.937613	0.310559	0.607366	-0.647164	-0.456629	0.354090	0.159520	-0.003391	...	2.005546	0.189726	-0.866309	-0.013960	1.095491	0.633077	-0.929036	0.248389	0.384225	2.123197
ZNF350	-0.291255	-0.487067	0.055948	0.097250	-0.280469	-0.122833	-0.056131	1.037237	1.439282	0.174373	...	-0.533165	1.758405	-0.883773	-0.953099	0.085036	-0.181022	-1.245042	-0.120807	1.457714	-0.632805
ZNF407	0.606737	-0.654086	0.305532	0.484507	-0.961143	-1.785327	-0.477502	-0.436590	0.140264	-1.367092	...	-0.308392	0.605374	0.988346	0.649056	1.850377	0.433976	1.592692	-0.192318	1.434121	-1.548050
ZNF436	3.826915	1.688653	-0.211357	1.565020	-0.649757	0.278732	0.413508	0.607107	1.101567	0.305156	...	-0.006953	1.052326	-1.156208	1.885187	-0.198600	0.433976	-0.851615	0.642532	2.598612	-0.918819
ZNF44	-0.126349	0.746189	-2.404442	0.945408	0.129994	-0.580633	2.089858	2.089790	1.140078	-0.409708	...	-0.644680	1.141020	-2.088775	1.263911	-0.754054	-0.213468	-1.437806	0.411368	1.088649	0.231104
ZNF467	0.350382	-0.333470	0.436969	0.558150	-0.624023	0.056168	0.966638	1.401582	1.181552	0.018195	...	0.101077	-0.178966	0.904520	-0.002722	0.117536	-0.514331	-0.022099	-0.441775	-0.449963	-0.889484
ZNF48	-0.336230	1.424703	-1.849156	-0.061463	-1.598068	-0.916458	-0.267469	-1.233595	1.052687	0.248018	...	0.559334	-2.610244	-1.987485	-1.378520	0.006741	1.206780	-0.036319	-0.162383	0.261203	1.174217
ZNF514	0.696686	-0.291715	1.117785	-0.790269	0.338442	1.690940	0.744864	0.154206	0.236542	-1.298526	...	1.040243	-0.535484	-0.059482	-0.646474	-0.288713	-0.790121	-0.284384	-0.509960	-0.281440	-0.354125
ZNF525	-0.000420	1.217421	-0.449126	-0.603624	0.907171	0.441100	-3.477973	0.467948	-0.358902	-0.987439	...	-0.095817	-0.283312	1.026767	0.493335	1.642082	-0.539403	0.110624	0.170226	0.141552	-1.597919
ZNF526	-1.776915	0.431537	-1.352943	-2.164083	-0.050147	-0.168771	-0.297474	2.483232	-0.031556	0.637828	...	-1.615210	-1.015479	-0.674207	0.997420	-0.377350	-0.226741	-0.406046	0.032193	0.230869	-0.668007
ZNF550	0.587248	-1.717341	-1.639447	-0.197320	0.204623	1.281455	0.361325	-0.330322	1.334116	-2.740951	...	-0.513998	-1.525038	-1.467064	-0.627209	0.631627	-0.343252	-0.290704	-0.129123	0.229184	-0.534534
ZNF580	-1.061819	-1.553304	0.349836	0.681311	0.534023	0.676336	-0.193110	0.528672	0.523896	-0.151950	...	0.521001	0.417550	-1.695840	0.003699	0.733559	-0.529079	1.012821	0.098715	-0.362331	0.297107
ZNF587B	0.995017	-1.232688	-0.242370	0.631792	-2.316057	-0.867351	-1.264147	0.059325	0.563889	-0.287813	...	1.041986	-0.279834	-1.625984	-0.171286	1.195945	0.078546	1.061802	-0.358623	0.067402	0.459915
ZNF627	1.459754	-2.203485	-0.555457	0.866686	0.510862	0.310413	-0.310519	-0.317671	1.931042	-0.997597	...	-1.075059	1.040152	-1.049679	0.324772	0.999468	-0.461237	1.875518	0.294955	-0.559502	0.135766
ZNF654	0.077536	1.086191	-0.694279	0.292783	-0.864639	-0.726368	-0.148755	-0.790814	0.315046	-0.233214	...	0.559334	-0.613744	1.262528	0.263768	0.223900	0.323365	-0.104260	-1.873659	-0.003378	0.772331
ZNF668	0.025066	0.243640	-1.472565	1.052062	-1.212053	-0.930715	-0.559689	-1.968610	1.301529	-0.365267	...	1.862667	0.561896	-0.347634	0.472466	0.607991	0.478221	0.483511	-1.705692	0.822383	0.628590
ZNF684	-1.902844	1.047419	-1.310115	1.043174	0.330722	-0.438857	-0.803640	2.020210	-1.179491	-0.158299	...	-0.628998	-0.429398	0.394577	-1.686750	-0.649168	0.131639	0.518272	0.351498	-0.239309	0.502451
ZNF746	0.723671	-0.096362	1.072004	1.312350	-0.712806	-0.457074	0.412203	-2.807363	0.218768	0.204847	...	-0.217786	0.189726	0.677491	-1.213168	0.482423	0.759911	0.570413	0.341520	-0.872954	-0.062244
ZNF750	-0.231289	0.043814	1.165044	1.321238	1.281606	0.408626	0.696595	0.279450	0.409843	1.120329	...	0.379865	-0.178966	0.387592	0.824041	-0.191213	-0.079260	0.180145	-0.049296	1.365026	1.448497
ZNF76	1.011508	0.057235	-0.066628	0.592432	0.705157	0.807023	1.021429	-0.751596	-0.785490	-1.553744	...	-0.451271	0.850589	0.148337	0.437148	1.243218	-0.787172	-0.824754	0.389748	0.160089	-0.280788
ZNF768	-0.270267	0.267500	-0.553980	-0.535060	-0.746261	-1.489895	-0.675794	-0.265803	-0.970640	-0.195122	...	0.845092	0.267986	0.338693	0.637818	-0.687577	0.217178	0.251246	0.291629	0.045494	0.778198
ZNF77	-0.950882	0.883383	0.897738	-0.608703	0.106833	-0.191741	0.536135	0.746267	1.614064	-0.314477	...	-0.338014	1.141020	-1.060157	0.849727	0.771968	0.400056	0.069543	1.103196	-1.472895	1.294490
ZNF800	-1.790407	1.162245	0.261227	0.988577	-1.190179	-1.484351	0.237393	-0.563098	0.282460	-0.464307	...	-0.400741	-0.798090	0.221685	0.456412	0.353900	-0.117605	0.878519	-0.784363	0.677454	-0.317456
ZNF837	0.034061	-0.709262	0.032319	0.200095	-0.055294	0.658119	0.579186	0.204810	1.984365	0.409275	...	0.102819	-0.773742	0.908013	-0.384799	-0.723032	-0.307856	-0.145341	-0.373590	0.281426	-0.141448
ZNF92	-0.006416	-1.032861	-0.855253	-0.357302	1.158081	-0.052341	-0.427929	0.097277	-1.012114	1.531725	...	0.648198	0.554940	-0.244598	-1.669091	-0.378827	-1.506882	0.698395	0.025541	-0.942049	-1.461513
ZNRD1ASP	-0.139841	-1.351987	0.498996	0.758762	-0.485057	-0.116497	0.251743	0.728556	1.205251	0.811782	...	-0.972256	0.920153	2.029189	1.593011	0.181059	-0.834366	-0.557729	-1.188484	-0.758359	-0.572669
ZSCAN18	0.854097	1.491809	-2.270051	-0.130026	-1.626376	0.959095	0.450035	-1.371489	0.914935	1.535534	...	0.031380	-0.723308	0.520316	-0.885673	0.308105	0.231927	-0.491368	1.532262	-1.016199	1.696376
ZSWIM1	1.867524	-0.296189	0.364605	-1.952043	-0.944416	0.572579	0.510044	-0.186102	0.005474	-0.028786	...	0.073198	0.471462	-0.847099	0.475676	-1.095304	-0.060087	1.488410	-0.215601	-1.644788	1.298890
ZXDC	-0.838446	-0.640665	0.109114	-0.150342	0.649828	0.040327	-0.669271	0.219991	-0.868437	-0.141793	...	-0.989680	-0.728525	0.129127	-1.333570	0.729127	-0.207569	0.372909	-0.188992	-0.552761	-0.288122
ZYX	0.210961	0.608994	1.141415	-0.149072	-0.477337	-0.407968	-0.504898	-0.421409	-0.869918	-0.379234	...	1.667516	0.304508	0.230417	-0.468278	0.463218	0.746638	0.363429	0.650847	0.849347	0.983541

	0	nameofSE	num_down_genes
18	OVCAR3_E10_20_chr19_rank_deviation_in_rank	OVCAR3_E10_20_chr19_rank_deviation_in_rank	2
55	OVCAR3_B6_41_chr4_rank_deviation_in_rank	OVCAR3_B6_41_chr4_rank_deviation_in_rank	2
53	OVCAR3_B7_99_chr5_rank_deviation_in_rank	OVCAR3_B7_99_chr5_rank_deviation_in_rank	2
4	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	2
42	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	2
14	OVCAR3_E9_85_chr9_rank_deviation_in_rank	OVCAR3_E9_85_chr9_rank_deviation_in_rank	2
8	OVCAR3_G8_23_chr20_rank_deviation_in_rank	OVCAR3_G8_23_chr20_rank_deviation_in_rank	2
23	OVCAR3_G10_8_chr20_rank_deviation_in_rank	OVCAR3_G10_8_chr20_rank_deviation_in_rank	2
10	OVCAR3_F12_94_chr3_rank_deviation_in_rank	OVCAR3_F12_94_chr3_rank_deviation_in_rank	2
28	OVCAR3_B10_45_chr1_rank_deviation_in_rank	OVCAR3_B10_45_chr1_rank_deviation_in_rank	2

	0	1	2	3	4	5
0	chr1	121484433	121485420	1_565_lociStitched	96953.691291	.
1	chr6	58776479	58779382	2_10320_lociStitched	77631.636248	.
2	chr19	40921118	40940761	6_5298_lociStitched	33143.175222	.
3	chr19	30149220	30182121	10_5176_lociStitched	27953.127688	.
4	chr7	61967557	61970462	3_10765_lociStitched	27109.740890	.
5	chr3	134027079	134056695	7_8070_lociStitched	27016.435985	.
6	chr12	12869283	12878497	5_2430_lociStitched	24663.208840	.
7	chr20	55858616	55865992	8_6950_lociStitched	24389.298604	.
8	chr12	52538601	52558687	7_2756_lociStitched	23199.478871	.
9	chr20	30176094	30200994	17_6601_lociStitched	22863.684827	.

	0	names	NumGenesDown	SELength	SERank_H3K27ac
0	OVCAR3_B4_24_chr11_rank_deviation_in_rank	OVCAR3_B4_24_chr11_rank_deviation_in_rank	3	10891	24
1	OVCAR3_C6_26_chr17_rank_deviation_in_rank	OVCAR3_C6_26_chr17_rank_deviation_in_rank	3	72939	26
2	OVCAR3_F7_106_chr4_rank_deviation_in_rank	OVCAR3_F7_106_chr4_rank_deviation_in_rank	6	25046	106
3	OVCAR3_G7_100_chr9_rank_deviation_in_rank	OVCAR3_G7_100_chr9_rank_deviation_in_rank	4	18655	100
4	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	2	24771	49
5	OVCAR3_D6_96_chr3_rank_deviation_in_rank	OVCAR3_D6_96_chr3_rank_deviation_in_rank	7	14085	96
6	OVCAR3_G4_92_chr15_rank_deviation_in_rank	OVCAR3_G4_92_chr15_rank_deviation_in_rank	3	37677	92
7	OVCAR3_G8_23_chr20_rank_deviation_in_rank	OVCAR3_G8_23_chr20_rank_deviation_in_rank	2	30497	23
8	OVCAR3_B11_65_chr5_rank_deviation_in_rank	OVCAR3_B11_65_chr5_rank_deviation_in_rank	3	22896	65
9	OVCAR3_F12_94_chr3_rank_deviation_in_rank	OVCAR3_F12_94_chr3_rank_deviation_in_rank	2	22089	94

	OVCAR3_H10_DORM_1	OVCAR3_F9_83	OVCAR3_B4_24	OVCAR3_C6_26	OVCAR3_A9_6	OVCAR3_D11_SCRAMBLE_2	OVCAR3_H6_63	OVCAR3_F7_106	OVCAR3_G7_100	OVCAR3_H12_49	...	OVCAR3_D1_5	OVCAR3_C11_16	OVCAR3_F4_47	OVCAR3_E5_90	OVCAR3_H1_RNF4_GENE	OVCAR3_C2_31	OVCAR3_D8_48	OVCAR3_E12_28	OVCAR3_B5_10	OVCAR3_A4_81
0	0.254764	0.256756	0.433225	0.497517	0.306456	0.304416	0.244923	0.509660	0.259447	0.508042	...	0.298367	0.268723	0.558895	0.295041	0.53814	1.429245	0.304490	0.293846	0.809102	0.266738
1	1.019056	2.310800	3.465802	1.741309	1.532278	0.913247	0.979693	1.274151	1.297236	2.794228	...	2.685305	7.255529	1.956134	3.245456	2.15256	3.096696	2.435922	1.469228	2.427307	2.934119
2	0.254764	0.256756	0.216613	0.248758	0.306456	0.304416	0.244923	0.254830	0.259447	0.254021	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.539402	0.266738
3	0.254764	0.256756	0.216613	0.497517	0.306456	0.304416	0.244923	0.254830	0.259447	0.254021	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.608980	0.293846	0.269701	0.266738
4	0.509528	0.256756	0.216613	0.248758	0.306456	1.217662	1.224616	0.254830	0.259447	0.762062	...	0.298367	0.537447	0.279448	0.295041	0.53814	0.476415	0.304490	0.293846	0.269701	0.266738
5	0.254764	0.256756	0.216613	0.248758	0.306456	0.304416	0.244923	0.254830	0.259447	0.254021	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.269701	0.266738
6	0.254764	0.256756	0.216613	0.248758	0.612911	0.304416	0.244923	0.254830	0.259447	0.254021	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.269701	0.266738
7	2.292875	0.770267	1.732901	2.736342	1.225822	1.217662	0.734770	0.764491	2.075578	0.508042	...	2.685305	3.493403	0.558895	1.475207	0.80721	1.429245	1.522451	1.175383	0.809102	2.667381
8	16.304889	14.635067	14.079821	11.194127	10.725944	14.611948	17.634473	12.996339	14.788493	16.003308	...	14.321625	13.973611	12.854595	15.047115	11.83908	13.816031	15.833490	16.161512	15.372944	13.336904
9	50.443251	56.742979	50.903968	41.293892	35.242388	49.619739	51.433879	47.143583	52.667792	57.154673	...	56.093031	50.519980	48.344454	48.386802	60.80982	51.214596	44.760059	41.138393	60.143270	59.749330

	Gene	OVCAR3_H10_DORM_1	OVCAR3_F9_83	OVCAR3_B4_24	OVCAR3_C6_26	OVCAR3_A9_6	OVCAR3_D11_SCRAMBLE_2	OVCAR3_H6_63	OVCAR3_F7_106	OVCAR3_G7_100	...	OVCAR3_D1_5	OVCAR3_C11_16	OVCAR3_F4_47	OVCAR3_E5_90	OVCAR3_H1_RNF4_GENE	OVCAR3_C2_31	OVCAR3_D8_48	OVCAR3_E12_28	OVCAR3_B5_10	OVCAR3_A4_81
0	A1BG	0.254764	0.256756	0.433225	0.497517	0.306456	0.304416	0.244923	0.509660	0.259447	...	0.298367	0.268723	0.558895	0.295041	0.53814	1.429245	0.304490	0.293846	0.809102	0.266738
1	A1BG-AS1	1.019056	2.310800	3.465802	1.741309	1.532278	0.913247	0.979693	1.274151	1.297236	...	2.685305	7.255529	1.956134	3.245456	2.15256	3.096696	2.435922	1.469228	2.427307	2.934119
2	A1CF	0.254764	0.256756	0.216613	0.248758	0.306456	0.304416	0.244923	0.254830	0.259447	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.539402	0.266738
3	A2M	0.254764	0.256756	0.216613	0.497517	0.306456	0.304416	0.244923	0.254830	0.259447	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.608980	0.293846	0.269701	0.266738
4	A2M-AS1	0.509528	0.256756	0.216613	0.248758	0.306456	1.217662	1.224616	0.254830	0.259447	...	0.298367	0.537447	0.279448	0.295041	0.53814	0.476415	0.304490	0.293846	0.269701	0.266738
5	A2ML1	0.254764	0.256756	0.216613	0.248758	0.306456	0.304416	0.244923	0.254830	0.259447	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.269701	0.266738
6	A2MP1	0.254764	0.256756	0.216613	0.248758	0.612911	0.304416	0.244923	0.254830	0.259447	...	0.298367	0.268723	0.279448	0.295041	0.26907	0.238207	0.304490	0.293846	0.269701	0.266738
7	A4GALT	2.292875	0.770267	1.732901	2.736342	1.225822	1.217662	0.734770	0.764491	2.075578	...	2.685305	3.493403	0.558895	1.475207	0.80721	1.429245	1.522451	1.175383	0.809102	2.667381
8	AAAS	16.304889	14.635067	14.079821	11.194127	10.725944	14.611948	17.634473	12.996339	14.788493	...	14.321625	13.973611	12.854595	15.047115	11.83908	13.816031	15.833490	16.161512	15.372944	13.336904
9	AACS	50.443251	56.742979	50.903968	41.293892	35.242388	49.619739	51.433879	47.143583	52.667792	...	56.093031	50.519980	48.344454	48.386802	60.80982	51.214596	44.760059	41.138393	60.143270	59.749330

	OVCAR3_H10_DORM_1_LFC	OVCAR3_F9_83_LFC	OVCAR3_B4_24_LFC	OVCAR3_C6_26_LFC	OVCAR3_A9_6_LFC	OVCAR3_D11_SCRAMBLE_2_LFC	OVCAR3_H6_63_LFC	OVCAR3_F7_106_LFC	OVCAR3_G7_100_LFC	OVCAR3_H12_49_LFC	...	OVCAR3_C11_16_LFC	OVCAR3_F4_47_LFC	OVCAR3_E5_90_LFC	OVCAR3_H1_RNF4_GENE_LFC	OVCAR3_C2_31_LFC	OVCAR3_D8_48_LFC	OVCAR3_E12_28_LFC	OVCAR3_B5_10_LFC	OVCAR3_A4_81_LFC	Gene
0	-0.219883	-0.208648	0.546074	0.745702	0.046634	0.036999	-0.276714	0.780493	-0.193602	0.775903	...	-0.142922	0.913535	-0.008126	0.858938	2.268137	0.037352	-0.013985	1.447279	-0.153620	A1BG
1	-1.059258	0.121902	0.706699	-0.286319	-0.470813	-1.217414	-1.116089	-0.736955	-0.711049	0.395959	...	1.772590	-0.118486	0.611931	0.019563	0.544239	0.197977	-0.531432	0.192866	0.466437	A1BG-AS1
2	-0.074221	-0.062987	-0.308265	-0.108637	0.192296	0.182660	-0.131053	-0.073846	-0.047941	-0.078436	...	0.002739	0.059196	0.137535	0.004599	-0.171164	0.183014	0.131676	1.007977	-0.007958	A1CF
3	-0.080511	-0.069276	-0.314554	0.885074	0.186006	0.176371	-0.137342	-0.080135	-0.054230	-0.084725	...	-0.003550	0.052907	0.131246	-0.001690	-0.177453	1.176724	0.125387	0.001688	-0.014248	A2M
4	0.839218	-0.149547	-0.394825	-0.195197	0.105735	2.096100	2.104315	-0.160406	-0.134501	1.419966	...	0.916179	-0.027364	0.050975	0.918039	0.742276	0.096453	0.045116	-0.078583	-0.094519	A2M-AS1
5	-0.076999	-0.065764	-0.311043	-0.111415	0.189518	0.179882	-0.133830	-0.076624	-0.050719	-0.081214	...	-0.000039	0.056418	0.134758	0.001822	-0.173942	0.180236	0.128899	0.005200	-0.010736	A2ML1
6	-0.074221	-0.062987	-0.308265	-0.108637	1.192296	0.182660	-0.131053	-0.073846	-0.047941	-0.078436	...	0.002739	0.059196	0.137535	0.004599	-0.171164	0.183014	0.131676	0.007977	-0.007958	A2MP1
7	0.645113	-0.928614	0.241145	0.900204	-0.258295	-0.267930	-0.996680	-0.939474	0.501469	-1.529026	...	1.252589	-1.391394	0.008873	-0.861028	-0.036792	0.054351	-0.318914	-0.857650	0.863379	A4GALT
8	0.234100	0.078224	0.022424	-0.308463	-0.370100	0.075944	0.347194	-0.093100	0.093270	0.207165	...	0.011500	-0.108921	0.118282	-0.227648	-0.004862	0.191774	0.221357	0.149189	-0.055781	AAAS
9	0.041561	0.211341	0.054677	-0.247172	-0.475789	0.017814	0.069618	-0.056039	0.103820	0.221771	...	0.043753	-0.019750	-0.018487	0.311204	0.063454	-0.130889	-0.252615	0.295303	0.285822	AACS

	OVCAR3_A10_87_chr6_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A12_55_chr1_rank_deviation_in_rank	OVCAR3_A1_17_chr17_rank_deviation_in_rank	OVCAR3_A2_29_chr19_rank_deviation_in_rank	OVCAR3_A3_60_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank	OVCAR3_A5_21_chr20_rank_deviation_in_rank	OVCAR3_A6_39_chr10_rank_deviation_in_rank	OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	...	OVCAR3_H11_18_LFC	OVCAR3_H12_49_LFC	OVCAR3_H1_RNF4_GENE_LFC	OVCAR3_H2_79_LFC	OVCAR3_H3_43_LFC	OVCAR3_H4_86_LFC	OVCAR3_H5_88_LFC	OVCAR3_H6_63_LFC	OVCAR3_H8_36_LFC	OVCAR3_H9_25_LFC
Gene
ABCC4	-92.0	-29.0	-688.0	-996.0	409.0	179.0	699.0	1046.0	-123.0	-974.0	...	0.150653	0.296848	-0.120546	-0.014040	-0.334427	0.223979	-0.256340	0.127717	0.223290	-0.005511
ABHD17C	541.0	74.0	531.0	469.0	-498.0	-1133.0	176.0	-949.0	-103.0	-1082.0	...	0.007009	0.277698	0.400626	-0.152054	0.182840	-0.006019	0.395992	0.378432	0.332829	0.191389
ABHD4	1270.0	1204.0	306.0	-463.0	530.0	277.0	854.0	-184.0	447.0	-452.0	...	-0.098348	-0.488206	-0.405171	0.607191	0.435937	-1.758377	0.476737	0.781105	0.501036	-0.324902
AC004870.2	-20.0	-92.0	31.0	1025.0	2210.0	1431.0	1565.0	-255.0	1340.0	1089.0	...	0.152149	-0.537270	-0.231842	-0.306943	-0.520017	-1.070475	-0.835361	-1.589886	-0.635490	-0.636999
AC004980.1	306.0	987.0	-1002.0	628.0	643.0	-443.0	307.0	-516.0	-414.0	472.0	...	0.565652	0.105716	-0.091357	0.113650	-0.057604	0.070010	-0.431842	-0.864439	-0.379528	-0.233480
AC006499.7	1807.0	555.0	-416.0	-751.0	443.0	-1108.0	11.0	261.0	161.0	244.0	...	-0.140708	0.067512	-0.006179	0.479836	0.626658	0.294840	0.468554	0.613932	0.071653	-0.128726
AC007996.1	-461.0	-676.0	668.0	1074.0	-152.0	-482.0	-940.0	-998.0	936.0	-877.0	...	0.234846	0.028554	0.096159	0.051755	0.163643	-0.137994	0.173618	-0.186334	0.202294	0.063535
AC008736.1	304.0	-607.0	-769.0	-283.0	-88.0	499.0	-532.0	-210.0	29.0	635.0	...	-0.178451	-0.135803	-0.345549	-0.250725	-0.273829	-0.594419	0.021785	-0.075209	-0.120812	-0.002245
AC008982.2	606.0	1513.0	-58.0	-201.0	-342.0	623.0	-48.0	1014.0	290.0	938.0	...	-0.115250	0.320862	-0.859137	0.029236	-0.117565	-0.176719	-0.392267	-0.316717	-0.233037	-1.930871
AC009404.1	-501.0	219.0	664.0	-161.0	-537.0	607.0	194.0	1973.0	-255.0	1220.0	...	-0.325949	-0.545882	-0.047809	-0.248441	-0.698555	0.183947	0.249136	0.138467	-0.229065	0.617423

	OVCAR3_A10_87_chr6_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A12_55_chr1_rank_deviation_in_rank	OVCAR3_A1_17_chr17_rank_deviation_in_rank	OVCAR3_A2_29_chr19_rank_deviation_in_rank	OVCAR3_A3_60_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank	OVCAR3_A5_21_chr20_rank_deviation_in_rank	OVCAR3_A6_39_chr10_rank_deviation_in_rank	OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	OVCAR3_A8_50_chr7_rank_deviation_in_rank	OVCAR3_A9_6_chr3_rank_deviation_in_rank	OVCAR3_B10_45_chr1_rank_deviation_in_rank	OVCAR3_B11_65_chr5_rank_deviation_in_rank	OVCAR3_B12_95_chr5_rank_deviation_in_rank	OVCAR3_B1_102_chr11_rank_deviation_in_rank	OVCAR3_B2_73_chr4_rank_deviation_in_rank	OVCAR3_B3_78_chr6_rank_deviation_in_rank	OVCAR3_B4_24_chr11_rank_deviation_in_rank	OVCAR3_B5_10_chr20_rank_deviation_in_rank	OVCAR3_B6_41_chr4_rank_deviation_in_rank	OVCAR3_B7_99_chr5_rank_deviation_in_rank	OVCAR3_B8_EMPTY_VECTOR_no_chro_rank_deviation_in_rank	OVCAR3_B9_91_chr9_rank_deviation_in_rank	OVCAR3_C10_105_chr18_rank_deviation_in_rank	OVCAR3_C11_16_chr17_rank_deviation_in_rank	OVCAR3_C12_51_chr21_rank_deviation_in_rank	OVCAR3_C1_89__chr12_rank_deviation_in_rank	OVCAR3_C2_31_chr12_rank_deviation_in_rank	OVCAR3_C3_54_chr3_rank_deviation_in_rank	OVCAR3_C4_22_chr5_rank_deviation_in_rank	OVCAR3_C5_33_chr17_rank_deviation_in_rank	OVCAR3_C6_26_chr17_rank_deviation_in_rank	OVCAR3_C7_77_chr1_rank_deviation_in_rank	OVCAR3_C8_FOXL2NB_GENE_chr3_rank_deviation_in_rank	OVCAR3_C9_2_chr6_rank_deviation_in_rank	OVCAR3_D10_67_chr8_rank_deviation_in_rank	OVCAR3_D11_SCRAMBLE_2_no_chro_rank_deviation_in_rank	OVCAR3_D12_71_chr8_rank_deviation_in_rank	OVCAR3_D1_5_chr7_rank_deviation_in_rank	OVCAR3_D2_98_chr4_rank_deviation_in_rank	OVCAR3_D3_44_chr6_rank_deviation_in_rank	OVCAR3_D4_84_chr8_rank_deviation_in_rank	OVCAR3_D5_80_chr6_rank_deviation_in_rank	OVCAR3_D6_96_chr3_rank_deviation_in_rank	OVCAR3_D7_14_chr1_rank_deviation_in_rank	OVCAR3_D8_48_chr1_rank_deviation_in_rank	OVCAR3_D9_59_chr2_rank_deviation_in_rank	OVCAR3_E10_20_chr19_rank_deviation_in_rank	OVCAR3_E11_12_chr19_rank_deviation_in_rank	OVCAR3_E12_28_chr3_rank_deviation_in_rank	OVCAR3_E1_PLAG1_GENE_chr8_rank_deviation_in_rank	OVCAR3_E2_62_chr2_rank_deviation_in_rank	OVCAR3_E3_30_chr5_rank_deviation_in_rank	OVCAR3_E4_52_chr20_rank_deviation_in_rank	OVCAR3_E5_90_chr12_rank_deviation_in_rank	OVCAR3_E6_75_chr9_rank_deviation_in_rank	OVCAR3_E7_72_chr15_rank_deviation_in_rank	OVCAR3_E8_42_chr1_rank_deviation_in_rank	OVCAR3_E9_85_chr9_rank_deviation_in_rank	OVCAR3_F10_19_chr11_rank_deviation_in_rank	OVCAR3_F11_4_chr19_rank_deviation_in_rank	OVCAR3_F12_94_chr3_rank_deviation_in_rank	OVCAR3_F1_101_chr5_rank_deviation_in_rank	OVCAR3_F3_38_chr3_rank_deviation_in_rank	OVCAR3_F4_47_chr12_rank_deviation_in_rank	OVCAR3_F5_FOX_ENH_chr3_rank_deviation_in_rank	OVCAR3_F6_56_chr5_rank_deviation_in_rank	OVCAR3_F7_106_chr4_rank_deviation_in_rank	OVCAR3_F8_9_chr12_rank_deviation_in_rank	OVCAR3_F9_83_chr12_rank_deviation_in_rank	OVCAR3_G10_8_chr20_rank_deviation_in_rank	OVCAR3_G11_SCRAMBLE_1_no_chro_rank_deviation_in_rank	OVCAR3_G12_104__chr6_rank_deviation_in_rank	OVCAR3_G1_82_chr12_rank_deviation_in_rank	OVCAR3_G2_32_chr20_rank_deviation_in_rank	OVCAR3_G3_53_chr3_rank_deviation_in_rank	OVCAR3_G4_92_chr15_rank_deviation_in_rank	OVCAR3_G5_58_chr20_rank_deviation_in_rank	OVCAR3_G6_69_chr1_rank_deviation_in_rank	OVCAR3_G7_100_chr9_rank_deviation_in_rank	OVCAR3_G8_23_chr20_rank_deviation_in_rank	OVCAR3_G9_13_chr11_rank_deviation_in_rank	OVCAR3_H10_DORM_1_no_chro_rank_deviation_in_rank	OVCAR3_H11_18_chr12_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_H2_79_chr3_rank_deviation_in_rank	OVCAR3_H3_43_chr11_rank_deviation_in_rank	OVCAR3_H4_86_chr5_rank_deviation_in_rank	OVCAR3_H5_88_chr4_rank_deviation_in_rank	OVCAR3_H6_63_chr2_rank_deviation_in_rank	OVCAR3_H8_36_chr14_rank_deviation_in_rank	OVCAR3_H9_25_chr22_rank_deviation_in_rank	OVCAR3_A10_87_LFC	OVCAR3_A11_RNF4_ENH_LFC	OVCAR3_A12_55_LFC	OVCAR3_A1_17_LFC	OVCAR3_A2_29_LFC	OVCAR3_A3_60_LFC	OVCAR3_A4_81_LFC	OVCAR3_A5_21_LFC	OVCAR3_A6_39_LFC	OVCAR3_A7_TP53_GENE_LFC	OVCAR3_A8_50_LFC	OVCAR3_A9_6_LFC	OVCAR3_B10_45_LFC	OVCAR3_B11_65_LFC	OVCAR3_B12_95_LFC	OVCAR3_B1_102_LFC	OVCAR3_B2_73_LFC	OVCAR3_B3_78_LFC	OVCAR3_B4_24_LFC	OVCAR3_B5_10_LFC	OVCAR3_B6_41_LFC	OVCAR3_B7_99_LFC	OVCAR3_B8_EMPTY_VECTOR_LFC	OVCAR3_B9_91_LFC	OVCAR3_C10_105_LFC	OVCAR3_C11_16_LFC	OVCAR3_C12_51_LFC	OVCAR3_C1_89_LFC	OVCAR3_C2_31_LFC	OVCAR3_C3_54_LFC	OVCAR3_C4_22_LFC	OVCAR3_C5_33_LFC	OVCAR3_C6_26_LFC	OVCAR3_C7_77_LFC	OVCAR3_C8_FOXL2NB_GENE_LFC	OVCAR3_C9_2_LFC	OVCAR3_D10_67_LFC	OVCAR3_D11_SCRAMBLE_2_LFC	OVCAR3_D12_71_LFC	OVCAR3_D1_5_LFC	OVCAR3_D2_98_LFC	OVCAR3_D3_44_LFC	OVCAR3_D4_84_LFC	OVCAR3_D5_80_LFC	OVCAR3_D6_96_LFC	OVCAR3_D7_14_LFC	OVCAR3_D8_48_LFC	OVCAR3_D9_59_LFC	OVCAR3_E10_20_LFC	OVCAR3_E11_12_LFC	OVCAR3_E12_28_LFC	OVCAR3_E1_PLAG1_GENE_LFC	OVCAR3_E2_62_LFC	OVCAR3_E3_30_LFC	OVCAR3_E4_52_LFC	OVCAR3_E5_90_LFC	OVCAR3_E6_75_LFC	OVCAR3_E7_72_LFC	OVCAR3_E8_42_LFC	OVCAR3_E9_85_LFC	OVCAR3_F10_19_LFC	OVCAR3_F11_4_LFC	OVCAR3_F12_94_LFC	OVCAR3_F1_101_LFC	OVCAR3_F3_38_LFC	OVCAR3_F4_47_LFC	OVCAR3_F5_FOX_ENH_LFC	OVCAR3_F6_56_LFC	OVCAR3_F7_106_LFC	OVCAR3_F8_9_LFC	OVCAR3_F9_83_LFC	OVCAR3_G10_8_LFC	OVCAR3_G11_SCRAMBLE_1_LFC	OVCAR3_G12_104_LFC	OVCAR3_G1_82_LFC	OVCAR3_G2_32_LFC	OVCAR3_G3_53_LFC	OVCAR3_G4_92_LFC	OVCAR3_G5_58_LFC	OVCAR3_G6_69_LFC	OVCAR3_G7_100_LFC	OVCAR3_G8_23_LFC	OVCAR3_G9_13_LFC	OVCAR3_H10_DORM_1_LFC	OVCAR3_H11_18_LFC	OVCAR3_H12_49_LFC	OVCAR3_H1_RNF4_GENE_LFC	OVCAR3_H2_79_LFC	OVCAR3_H3_43_LFC	OVCAR3_H4_86_LFC	OVCAR3_H5_88_LFC	OVCAR3_H6_63_LFC	OVCAR3_H8_36_LFC	OVCAR3_H9_25_LFC
OVCAR3_A10_87_chr6_rank_deviation_in_rank	1	0.1	0.0027	-0.082	-0.22	-0.2	-0.13	-0.2	-0.0064	-0.18	-0.063	-0.026	-0.079	-0.14	-0.031	-0.074	-0.096	-0.047	0.019	-0.0018	0.085	-0.039	-0.082	0.13	0.055	-0.19	-0.14	-0.02	-0.14	-0.18	-0.11	-0.18	-0.12	-0.16	-0.08	-0.0018	-0.15	-0.23	-0.17	-0.082	0.025	-0.14	-0.11	0.069	0.079	-0.22	-0.26	-0.14	0.13	0.15	0.11	0.17	0.19	0.17	0.11	0.17	0.075	0.19	0.11	0.21	0.099	0.02	-0.047	0.24	0.15	0.13	0.14	0.095	0.083	0.081	0.037	0.18	0.078	0.012	-0.092	0.13	0.087	-0.055	0.078	0.027	0.096	0.024	0.079	0.03	0.14	0.046	0.099	0.085	0.19	0.13	0.15	0.17	0.15	0.097	0.57	-0.079	-0.12	-0.22	-0.21	-0.16	-0.12	-0.2	-0.17	-0.19	-0.13	-0.11	-0.18	-0.13	-0.11	-0.15	-0.12	-0.12	-0.082	-0.074	-0.14	-0.19	-0.13	-0.094	-0.15	-0.15	-0.16	-0.091	-0.18	-0.16	-0.11	-0.14	-0.12	-0.16	-0.12	-0.14	-0.19	-0.19	-0.13	-0.17	-0.093	-0.12	-0.12	-0.087	-0.082	-0.22	-0.22	-0.17	0.16	0.12	0.13	0.099	0.1	0.14	0.097	0.13	0.066	0.12	0.13	0.17	0.15	0.058	0.06	0.17	0.083	0.12	0.14	0.1	0.11	0.14	0.093	0.12	0.16	0.12	-0.00015	0.16	0.12	0.068	0.13	0.011	0.16	0.1	0.13	0.14	0.19	0.14	0.17	0.18	0.19	0.16	0.17	0.22	0.16	0.15
OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	0.1	1	0.034	-0.049	-0.1	-0.11	-0.057	-0.011	-0.002	-0.024	-0.069	0.068	-0.003	-0.097	-0.15	-0.082	-0.0021	-0.036	-0.071	-0.052	0.13	-0.0032	-0.081	0.094	0.062	-0.0099	0.05	-0.13	-0.17	-0.2	-0.059	-0.15	-0.087	-0.14	-0.069	0.087	-0.11	-0.093	-0.084	0.093	0.14	-0.095	-0.093	0.12	0.064	-0.25	-0.1	-0.24	0.047	0.029	0.027	0.06	0.098	0.051	0.099	0.069	0.023	0.068	0.051	0.07	0.0071	0.087	-0.041	0.06	-0.011	-0.0015	0.033	0.1	0.17	0.05	0.075	0.11	0.12	0.069	-0.016	0.11	0.094	0.077	0.13	0.078	0.047	0.076	0.048	0.053	0.1	0.087	0.11	0.15	0.096	0.096	0.081	0.016	0.11	0.11	-0.044	0.62	-0.077	-0.15	-0.13	-0.096	-0.066	-0.063	-0.12	-0.061	-0.13	-0.0044	-0.095	-0.12	-0.11	-0.13	-0.054	-0.057	-0.077	-0.055	-0.019	-0.13	-0.15	-0.022	-0.053	-0.0084	0.0015	-0.13	-0.2	-0.19	-0.038	-0.14	-0.069	-0.14	-0.065	-0.012	-0.14	-0.08	-0.052	0.015	0.035	-0.11	-0.077	0.011	-0.027	-0.24	-0.11	-0.24	0.048	-0.014	0.047	-0.0065	0.027	0.013	0.071	0.04	-0.0061	0.0091	0.04	0.033	0.031	0.077	-0.00046	-0.0056	-0.056	-0.0059	0.026	0.057	0.16	0.054	0.071	0.068	0.15	0.092	-0.002	0.1	0.078	0.097	0.098	0.024	0.07	0.087	0.048	0.091	0.084	0.12	0.12	0.16	0.092	0.1	0.084	0.063	0.098	0.11
OVCAR3_A12_55_chr1_rank_deviation_in_rank	0.0027	0.034	1	-0.013	-0.036	-0.073	-0.035	-0.042	0.04	0.046	-0.093	0.085	0.0045	-0.0015	-0.0086	0.028	-0.048	0.082	0.038	0.11	0.067	0.00049	0.011	0.038	-0.071	0.041	0.025	-0.044	0.0029	-0.00075	-0.044	0.019	0.068	-0.097	-0.016	-0.061	0.037	0.042	-0.12	0.11	0.053	-0.041	-0.054	-0.078	0.018	-0.12	0.0088	-0.16	-0.031	0.064	0.094	-0.047	0.01	0.00049	0.056	-0.054	-0.11	0.0057	0.015	-0.0063	0.068	0.12	-0.015	-0.074	-0.0046	-0.067	-0.05	0.068	0.025	-0.021	-0.02	-0.0033	0.13	-0.026	-0.089	0.052	0.13	-0.011	0.11	-0.041	-0.024	0.098	0.0078	0.066	0.18	0.11	0.089	0.099	0.093	0.0084	0.073	0.13	0.13	0.085	-0.039	-0.033	0.68	-0.05	-0.079	-0.094	-0.06	-0.07	-0.02	-0.013	-0.11	0.017	-0.059	-0.044	0.024	-0.034	-0.078	0.069	0.048	0.1	0.064	-0.052	-0.028	0.042	-0.048	0.076	0.053	-0.011	-0.062	-0.036	0.00083	-0.01	0.1	-0.14	0.032	-0.017	-0.011	0.028	-0.067	0.088	0.055	-0.068	-0.013	-0.039	0.032	-0.12	-0.0088	-0.16	-0.056	0.033	0.045	-0.085	-0.028	-0.042	0.018	-0.087	-0.13	-0.044	0.00022	-0.047	0.041	0.071	-0.019	-0.11	-0.021	-0.072	-0.063	0.024	-0.0036	-0.049	-0.04	-0.051	0.075	-0.04	-0.094	0.03	0.094	-0.014	0.059	-0.084	-0.044	0.077	-0.025	0.015	0.098	0.053	0.063	0.048	0.072	-0.00051	0.064	0.094	0.062	0.032
OVCAR3_A1_17_chr17_rank_deviation_in_rank	-0.082	-0.049	-0.013	1	0.16	-0.051	-0.078	-0.065	0.21	0.07	0.05	0.056	-0.22	-0.37	-0.022	-0.19	-0.2	-0.28	-0.12	0.16	-0.031	-0.09	-0.048	0.017	-0.12	-0.055	0.044	-0.045	0.0011	-0.2	-0.23	-0.18	0.017	-0.32	0.1	-0.18	-0.23	-0.11	-0.14	0.32	0.1	-0.26	-0.17	-0.18	0.15	0.023	-0.13	-0.12	0.01	0.36	0.21	0.15	0.27	-0.047	0.29	-0.03	-0.022	0.079	0.013	0.051	0.2	0.38	0.14	-0.025	0.23	0.045	0.18	0.013	0.17	-0.0095	-0.0049	0.0061	0.15	-0.089	0.081	0.3	0.43	0.00051	0.2	0.099	-0.095	0.26	0.11	0.011	0.16	0.11	0.029	-0.23	0.2	-0.24	0.078	-0.082	0.085	-0.11	-0.21	-0.2	-0.17	0.57	0.049	-0.023	-0.12	-0.13	-0.064	-0.023	-0.052	-0.059	-0.28	-0.36	-0.053	-0.24	-0.24	-0.21	-0.1	0.097	-0.14	-0.23	-0.13	-0.083	-0.19	-0.0074	0.012	-0.053	-0.1	-0.21	-0.16	-0.19	0.034	-0.31	0.051	-0.19	-0.27	-0.11	-0.076	0.16	0.024	-0.27	-0.11	-0.2	0.036	-0.019	-0.14	-0.18	0.021	0.29	0.19	0.039	0.18	-0.087	0.23	-0.096	-0.075	0.0086	0.0082	-0.0051	0.2	0.32	0.14	-0.13	0.16	0.033	0.15	-0.013	0.16	0.037	0.041	-0.044	0.18	0.0033	0.074	0.28	0.38	0.067	0.16	0.00046	-0.025	0.25	0.13	0.1	0.16	0.14	0.1	-0.092	0.2	-0.14	0.085	-0.013	0.11	-0.011
OVCAR3_A2_29_chr19_rank_deviation_in_rank	-0.22	-0.1	-0.036	0.16	1	0.47	0.29	0.23	0.004	0.27	0.2	0.21	0.034	0.12	0.1	-0.023	0.19	0.064	0.038	0.22	-0.17	-0.13	0.16	-0.21	-0.28	0.31	0.22	0.16	0.2	0.27	0.14	0.3	0.32	0.17	0.2	-0.14	0.1	0.37	0.31	0.17	0.043	0.23	0.23	-0.13	-0.069	0.23	0.35	0.1	-0.13	-0.1	-0.031	-0.34	-0.28	-0.27	-0.14	-0.34	-0.22	-0.37	-0.21	-0.27	-0.071	-0.015	0.082	-0.38	-0.25	-0.27	-0.15	-0.24	-0.039	-0.18	-0.083	-0.36	0.0044	0.0054	0.12	-0.014	-0.011	0.12	-0.066	-0.13	-0.093	0.023	-0.073	0.019	-0.095	0.015	-0.09	-0.13	-0.18	-0.26	-0.16	-0.17	-0.11	-0.11	0.11	0.13	0.17	0.37	0.88	0.33	0.24	0.21	0.22	0.26	0.24	0.22	0.17	0.09	0.28	0.099	0.19	0.21	0.2	0.31	0.27	0.14	0.19	0.23	0.15	0.3	0.34	0.3	0.21	0.17	0.17	0.18	0.32	0.073	0.34	0.2	0.16	0.25	0.27	0.33	0.25	0.16	0.27	0.18	0.24	0.22	0.22	0.17	-0.26	-0.12	-0.19	-0.28	-0.21	-0.29	-0.18	-0.34	-0.29	-0.32	-0.28	-0.28	-0.24	-0.16	-0.18	-0.33	-0.19	-0.29	-0.22	-0.31	-0.21	-0.31	-0.28	-0.35	-0.22	-0.26	-0.087	-0.15	-0.14	-0.17	-0.24	-0.19	-0.27	-0.2	-0.26	-0.27	-0.25	-0.25	-0.3	-0.36	-0.25	-0.4	-0.3	-0.33	-0.26	-0.3
OVCAR3_A3_60_chr20_rank_deviation_in_rank	-0.2	-0.11	-0.073	-0.051	0.47	1	0.48	0.38	-0.088	0.33	0.33	0.26	0.1	0.39	0.22	0.19	0.36	0.22	0.23	0.19	-0.36	-0.12	0.29	-0.33	-0.31	0.5	0.21	0.29	0.34	0.57	0.41	0.57	0.51	0.5	0.26	-0.053	0.21	0.53	0.49	0.069	-0.0056	0.49	0.46	-0.13	-0.23	0.4	0.53	0.25	-0.25	-0.37	-0.23	-0.54	-0.58	-0.32	-0.37	-0.42	-0.26	-0.55	-0.29	-0.41	-0.21	-0.23	0.01	-0.53	-0.49	-0.37	-0.32	-0.36	-0.25	-0.25	-0.099	-0.55	-0.072	0.062	0.13	-0.15	-0.22	0.09	-0.23	-0.25	-0.046	-0.098	-0.19	0.017	-0.27	-0.057	-0.094	-0.029	-0.43	-0.23	-0.28	-0.18	-0.28	-0.079	0.32	0.31	0.32	0.42	0.48	0.87	0.42	0.42	0.37	0.39	0.43	0.37	0.35	0.38	0.41	0.36	0.4	0.37	0.36	0.31	0.34	0.32	0.41	0.28	0.28	0.4	0.38	0.41	0.4	0.45	0.35	0.4	0.44	0.38	0.41	0.38	0.32	0.34	0.37	0.35	0.3	0.39	0.43	0.28	0.23	0.41	0.35	0.37	-0.4	-0.36	-0.4	-0.41	-0.42	-0.34	-0.39	-0.39	-0.31	-0.45	-0.4	-0.39	-0.43	-0.4	-0.33	-0.41	-0.37	-0.4	-0.4	-0.43	-0.43	-0.42	-0.39	-0.49	-0.42	-0.32	-0.13	-0.33	-0.36	-0.32	-0.44	-0.28	-0.33	-0.41	-0.44	-0.42	-0.46	-0.43	-0.42	-0.41	-0.51	-0.46	-0.45	-0.43	-0.47	-0.39
OVCAR3_A4_81_chr7_rank_deviation_in_rank	-0.13	-0.057	-0.035	-0.078	0.29	0.48	1	0.23	-0.04	0.22	0.19	0.16	0.076	0.25	0.17	0.084	0.26	0.18	0.12	0.11	-0.25	-0.062	0.16	-0.19	-0.18	0.29	0.14	0.18	0.18	0.36	0.31	0.4	0.37	0.32	0.23	-0.1	0.13	0.34	0.32	0.027	-0.023	0.35	0.29	-0.07	-0.11	0.26	0.34	0.18	-0.15	-0.24	-0.15	-0.35	-0.4	-0.22	-0.27	-0.26	-0.19	-0.36	-0.17	-0.32	-0.1	-0.17	-0.023	-0.34	-0.31	-0.23	-0.11	-0.25	-0.14	-0.086	-0.036	-0.35	-0.054	0.025	0.11	-0.14	-0.17	0.042	-0.15	-0.18	0.0087	-0.14	-0.082	-0.006	-0.21	-0.048	0.0057	0.026	-0.25	-0.077	-0.12	-0.11	-0.18	-0.043	0.23	0.23	0.22	0.25	0.31	0.35	0.92	0.24	0.27	0.24	0.28	0.23	0.24	0.22	0.31	0.21	0.27	0.27	0.24	0.2	0.24	0.23	0.24	0.23	0.22	0.24	0.27	0.28	0.22	0.25	0.27	0.27	0.31	0.2	0.31	0.22	0.2	0.2	0.23	0.22	0.19	0.26	0.28	0.21	0.2	0.26	0.2	0.25	-0.26	-0.24	-0.29	-0.26	-0.3	-0.24	-0.29	-0.25	-0.24	-0.31	-0.29	-0.33	-0.28	-0.3	-0.27	-0.27	-0.23	-0.26	-0.22	-0.3	-0.28	-0.26	-0.26	-0.32	-0.32	-0.25	-0.11	-0.27	-0.28	-0.25	-0.3	-0.2	-0.23	-0.35	-0.27	-0.32	-0.37	-0.32	-0.27	-0.27	-0.33	-0.27	-0.28	-0.31	-0.35	-0.29
OVCAR3_A5_21_chr20_rank_deviation_in_rank	-0.2	-0.011	-0.042	-0.065	0.23	0.38	0.23	1	-0.13	0.21	0.14	0.15	0.046	0.28	0.036	0.11	0.18	0.12	0.086	0.041	-0.14	-0.051	0.2	-0.25	-0.081	0.37	0.15	0.016	0.095	0.25	0.28	0.3	0.25	0.24	0.04	-0.0093	0.15	0.35	0.3	0.096	0.11	0.35	0.25	0.036	-0.073	0.13	0.37	0.12	-0.24	-0.29	-0.21	-0.32	-0.34	-0.23	-0.24	-0.29	-0.18	-0.35	-0.13	-0.33	-0.035	-0.093	0.03	-0.32	-0.37	-0.23	-0.19	-0.13	-0.035	-0.015	-0.051	-0.28	-0.012	0.051	0.036	-0.15	-0.15	0.11	-0.087	-0.14	0.02	-0.05	-0.074	0.001	-0.19	-0.042	-0.005	0.085	-0.2	-0.048	-0.17	-0.11	-0.12	-0.065	0.13	0.22	0.18	0.23	0.23	0.25	0.19	0.88	0.17	0.24	0.2	0.22	0.19	0.26	0.21	0.21	0.19	0.23	0.21	0.18	0.27	0.22	0.24	0.16	0.26	0.3	0.25	0.17	0.14	0.18	0.26	0.18	0.25	0.16	0.21	0.26	0.21	0.2	0.24	0.25	0.27	0.28	0.25	0.27	0.21	0.13	0.26	0.19	-0.34	-0.29	-0.3	-0.26	-0.25	-0.25	-0.26	-0.3	-0.24	-0.3	-0.21	-0.33	-0.22	-0.22	-0.21	-0.26	-0.31	-0.24	-0.25	-0.22	-0.19	-0.17	-0.25	-0.26	-0.24	-0.2	-0.15	-0.27	-0.23	-0.18	-0.23	-0.18	-0.18	-0.26	-0.26	-0.3	-0.31	-0.29	-0.24	-0.21	-0.26	-0.23	-0.29	-0.27	-0.26	-0.29
OVCAR3_A6_39_chr10_rank_deviation_in_rank	-0.0064	-0.002	0.04	0.21	0.004	-0.088	-0.04	-0.13	1	0.057	-0.027	0.054	-0.24	-0.2	-0.073	-0.2	-0.22	-0.15	-0.0018	0.14	0.005	-0.031	-0.11	-0.033	-0.08	0.042	0.036	-0.1	0.037	-0.11	-0.17	-0.16	-0.029	-0.23	0.071	-0.055	-0.19	-0.053	-0.13	0.29	0.0068	-0.16	-0.078	-0.17	-0.0049	-0.062	-0.06	-0.12	-0.0012	0.23	0.14	0.15	0.14	-0.015	0.23	-0.042	-0.015	0.065	0.049	0.014	0.13	0.29	0.099	-0.0061	0.18	0.03	0.17	0.056	0.12	-0.03	0.05	0.049	0.1	-0.058	0.052	0.18	0.23	-0.032	0.16	-0.0056	-0.1	0.21	0.12	0.025	0.19	0.035	0.026	-0.09	0.12	-0.19	0.11	-0.015	0.11	-0.026	-0.14	-0.14	-0.095	-0.0082	-0.084	-0.053	-0.059	-0.17	0.56	-0.029	-0.1	-0.038	-0.26	-0.2	-0.072	-0.21	-0.23	-0.11	-0.0049	0.073	-0.089	-0.17	-0.15	-0.13	-0.15	0.077	0.002	-0.094	-0.056	-0.11	-0.11	-0.16	-0.015	-0.23	0.03	-0.11	-0.23	-0.015	-0.07	0.16	-0.033	-0.16	-0.043	-0.19	-0.069	-0.083	-0.055	-0.17	0.019	0.18	0.13	0.066	0.048	-0.036	0.17	-0.082	-0.053	-0.0021	0.037	-0.024	0.16	0.26	0.11	-0.091	0.11	0.021	0.14	0.027	0.14	0.0085	0.082	-0.0088	0.14	0.0088	0.051	0.16	0.19	0.042	0.14	-0.04	-0.016	0.21	0.13	0.11	0.17	0.089	0.099	0.0095	0.11	-0.085	0.12	0.059	0.11	0.05
OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	-0.18	-0.024	0.046	0.07	0.27	0.33	0.22	0.21	0.057	1	0.17	0.25	0.041	0.091	0.022	0.05	0.17	0.036	0.08	0.18	-0.12	-0.11	0.11	-0.19	-0.23	0.31	0.17	0.089	0.15	0.21	0.12	0.22	0.24	0.11	0.086	-0.078	0.072	0.3	0.18	0.28	0.15	0.18	0.15	-0.086	-0.05	0.07	0.32	-0.033	-0.19	-0.096	-0.018	-0.25	-0.19	-0.23	-0.075	-0.26	-0.22	-0.32	-0.18	-0.22	-0.037	0.041	0.1	-0.3	-0.25	-0.26	-0.13	-0.15	0.0024	-0.15	0.0091	-0.33	0.076	0.067	-0.015	0.008	0.014	0.069	0.0049	-0.092	-0.093	0.0046	-0.018	0.084	-0.0011	0.068	-0.038	-0.076	-0.13	-0.19	-0.12	-0.099	-0.058	0.04	0.07	0.14	0.18	0.25	0.24	0.21	0.17	0.17	0.22	0.88	0.2	0.25	0.14	0.075	0.19	0.11	0.17	0.16	0.19	0.25	0.23	0.085	0.12	0.15	0.11	0.28	0.26	0.2	0.15	0.13	0.15	0.1	0.24	0.044	0.23	0.18	0.12	0.19	0.15	0.36	0.27	0.12	0.19	0.15	0.18	0.082	0.22	0.046	-0.28	-0.12	-0.14	-0.21	-0.12	-0.26	-0.12	-0.27	-0.27	-0.29	-0.25	-0.23	-0.17	-0.1	-0.13	-0.27	-0.21	-0.25	-0.19	-0.23	-0.13	-0.25	-0.17	-0.32	-0.14	-0.15	-0.18	-0.13	-0.081	-0.14	-0.15	-0.17	-0.24	-0.16	-0.18	-0.19	-0.16	-0.16	-0.21	-0.29	-0.19	-0.3	-0.22	-0.24	-0.2	-0.15
OVCAR3_A8_50_chr7_rank_deviation_in_rank	-0.063	-0.069	-0.093	0.05	0.2	0.33	0.19	0.14	-0.027	0.17	1	0.19	0.018	0.061	0.055	0.046	0.15	0.032	0.16	0.17	-0.16	-0.14	0.1	-0.2	-0.18	0.25	0.075	0.15	0.1	0.23	0.15	0.2	0.19	0.17	0.13	-0.033	0.062	0.25	0.18	0.093	0.016	0.23	0.18	-0.12	-0.12	0.14	0.22	0.088	-0.16	-0.14	-0.057	-0.28	-0.18	-0.18	-0.14	-0.23	-0.094	-0.25	-0.12	-0.21	-0.036	-0.069	-0.027	-0.29	-0.17	-0.13	-0.12	-0.17	-0.061	-0.059	-0.063	-0.25	0.031	-0.019	0.026	0.0053	0.0046	0.03	-0.069	-0.13	0.024	-0.039	-0.036	0.032	-0.072	-0.034	0.018	-0.033	-0.084	-0.18	-0.077	-0.11	-0.088	0.0063	0.16	0.12	0.1	0.25	0.2	0.23	0.16	0.15	0.17	0.2	0.86	0.2	0.14	0.054	0.15	0.14	0.17	0.12	0.19	0.19	0.15	0.083	0.17	0.093	0.078	0.19	0.16	0.2	0.14	0.16	0.12	0.11	0.18	0.12	0.2	0.15	0.11	0.16	0.12	0.19	0.15	0.18	0.17	0.082	0.092	0.14	0.14	0.14	-0.23	-0.13	-0.15	-0.21	-0.078	-0.19	-0.15	-0.22	-0.11	-0.19	-0.16	-0.19	-0.15	-0.15	-0.18	-0.23	-0.11	-0.13	-0.15	-0.2	-0.15	-0.15	-0.18	-0.22	-0.15	-0.17	-0.1	-0.079	-0.061	-0.15	-0.16	-0.14	-0.11	-0.17	-0.16	-0.18	-0.16	-0.19	-0.15	-0.21	-0.13	-0.27	-0.17	-0.22	-0.17	-0.15
OVCAR3_A9_6_chr3_rank_deviation_in_rank	-0.026	0.068	0.085	0.056	0.21	0.26	0.16	0.15	0.054	0.25	0.19	1	0.0064	0.023	0.069	0.061	0.22	0.057	0.078	0.21	-0.069	-0.15	0.12	-0.095	-0.27	0.21	0.057	0.12	0.14	0.18	0.15	0.17	0.29	0.074	0.094	-0.13	0.0025	0.22	0.19	0.18	0.066	0.15	0.09	-0.13	-0.029	0.0014	0.2	-0.15	-0.14	-0.045	0.026	-0.25	-0.14	-0.21	-0.058	-0.24	-0.17	-0.25	-0.18	-0.15	-0.024	-0.03	-0.014	-0.26	-0.2	-0.21	-0.19	-0.13	-0.016	-0.13	0.01	-0.23	0.085	-0.0014	-0.034	0.047	0.013	0.069	-0.038	-0.13	-0.017	0.0048	-0.021	0.16	0.033	0.1	0.0012	-0.024	-0.046	-0.1	-0.05	-0.065	0.032	0.0065	0.12	0.15	0.16	0.19	0.17	0.13	0.11	0.11	0.15	0.21	0.17	0.84	0.066	-0.008	0.23	0.099	0.16	0.2	0.2	0.31	0.25	0.023	0.12	0.21	0.069	0.26	0.22	0.23	0.1	0.092	0.2	0.05	0.3	-0.021	0.25	0.16	0.03	0.098	0.2	0.31	0.23	0.079	0.17	0.11	0.2	0.0075	0.077	-0.081	-0.23	-0.063	-0.085	-0.25	-0.089	-0.23	-0.1	-0.25	-0.24	-0.24	-0.24	-0.18	-0.15	-0.14	-0.2	-0.24	-0.18	-0.21	-0.24	-0.22	-0.13	-0.2	-0.15	-0.24	-0.095	-0.17	-0.17	-0.07	-0.08	-0.13	-0.17	-0.2	-0.14	-0.14	-0.16	-0.091	-0.11	-0.12	-0.16	-0.21	-0.095	-0.22	-0.15	-0.19	-0.12	-0.17
OVCAR3_B10_45_chr1_rank_deviation_in_rank	-0.079	-0.003	0.0045	-0.22	0.034	0.1	0.076	0.046	-0.24	0.041	0.018	0.0064	1	0.26	-0.033	0.18	0.23	0.25	0.049	-0.096	0.046	0.031	0.068	0.025	0.02	0.13	0.048	0.084	-0.097	0.12	0.17	0.2	0.066	0.17	-0.1	0.15	0.11	0.13	0.19	-0.12	0.055	0.17	0.12	0.095	-0.04	-0.11	0.14	-0.082	0.014	-0.2	-0.098	-0.2	-0.22	-0.023	-0.17	-0.046	-0.051	-0.12	-0.084	-0.093	-0.087	-0.19	-0.087	-0.078	-0.22	-0.078	-0.18	-0.055	-0.072	-0.037	0.04	-0.053	0.0068	0.1	-0.053	-0.14	-0.2	0.064	-0.065	-0.058	0.056	-0.1	-0.12	0.078	-0.037	0.03	0.072	0.21	-0.11	0.2	-0.033	0.064	0.023	0.16	0.052	0.092	0.11	-0.028	0.065	0.052	0.058	0.076	-0.026	0.077	0.055	0.059	0.76	0.23	0.035	0.19	0.21	0.2	0.056	-0.036	0.14	0.13	0.1	0.13	0.13	0.081	0.081	0.096	-0.023	0.088	0.097	0.16	0.046	0.13	-0.022	0.19	0.13	0.096	0.12	-0.028	0.088	0.14	0.074	0.14	0.049	-0.085	0.1	-0.041	-0.059	-0.19	-0.15	-0.16	-0.19	-0.042	-0.17	-0.057	-0.057	-0.11	-0.12	-0.088	-0.15	-0.2	-0.16	-0.037	-0.2	-0.093	-0.2	-0.088	-0.14	-0.12	-0.079	-0.05	-0.1	-0.028	-0.088	-0.18	-0.22	-0.074	-0.13	-0.045	-0.067	-0.16	-0.2	-0.08	-0.12	-0.084	-0.057	0.0093	-0.17	0.051	-0.086	-0.052	-0.069	0.018
OVCAR3_B11_65_chr5_rank_deviation_in_rank	-0.14	-0.097	-0.0015	-0.37	0.12	0.39	0.25	0.28	-0.2	0.091	0.061	0.023	0.26	1	0.11	0.25	0.32	0.39	0.16	-0.045	-0.079	0.041	0.15	-0.15	-0.063	0.36	0.11	0.14	0.11	0.43	0.39	0.44	0.27	0.49	0.016	0.11	0.32	0.41	0.38	-0.18	-0.039	0.41	0.33	0.03	-0.23	0.17	0.39	0.2	-0.093	-0.44	-0.21	-0.42	-0.48	-0.14	-0.41	-0.19	-0.17	-0.32	-0.18	-0.33	-0.19	-0.32	-0.11	-0.3	-0.47	-0.2	-0.3	-0.22	-0.24	-0.071	-0.036	-0.25	-0.065	0.17	-0.09	-0.29	-0.39	0.057	-0.24	-0.24	0.04	-0.22	-0.21	0.075	-0.2	-0.032	0.038	0.2	-0.31	0.12	-0.13	0.034	-0.18	0.076	0.24	0.23	0.26	0.081	0.18	0.26	0.23	0.32	0.21	0.19	0.18	0.15	0.38	0.89	0.22	0.33	0.33	0.39	0.22	0.06	0.29	0.33	0.26	0.21	0.27	0.24	0.2	0.21	0.21	0.37	0.28	0.33	0.2	0.37	0.13	0.32	0.36	0.29	0.25	0.047	0.14	0.35	0.26	0.25	0.076	0.2	0.28	0.28	-0.2	-0.39	-0.33	-0.3	-0.35	-0.14	-0.39	-0.16	-0.18	-0.23	-0.25	-0.29	-0.33	-0.39	-0.29	-0.18	-0.4	-0.21	-0.34	-0.25	-0.34	-0.22	-0.24	-0.21	-0.3	-0.12	-0.23	-0.38	-0.44	-0.22	-0.33	-0.2	-0.19	-0.38	-0.35	-0.24	-0.33	-0.28	-0.23	-0.12	-0.35	-0.11	-0.26	-0.18	-0.31	-0.19
OVCAR3_B12_95_chr5_rank_deviation_in_rank	-0.031	-0.15	-0.0086	-0.022	0.1	0.22	0.17	0.036	-0.073	0.022	0.055	0.069	-0.033	0.11	1	0.0035	0.021	0.013	0.072	-0.045	-0.14	-0.011	0.11	-0.059	-0.16	0.016	-0.01	0.19	0.25	0.25	0.18	0.26	0.2	0.25	0.09	-0.082	0.1	0.12	0.21	-0.12	-0.17	0.13	0.15	-0.11	-0.07	0.3	0.12	0.2	-0.008	-0.048	-0.027	-0.13	-0.18	-0.052	-0.15	-0.066	0.019	-0.083	-0.067	-0.085	-0.097	-0.11	-0.011	-0.1	-0.077	-0.011	-0.12	-0.18	-0.13	-0.015	-0.095	-0.16	-0.11	0.018	0.15	-0.036	-0.15	-0.011	-0.11	-0.086	0.04	-0.069	-0.082	0.028	-0.082	0.005	-0.075	-0.019	-0.18	-0.023	-0.096	0.031	-0.081	-0.062	0.081	0.0002	0.089	0.082	0.12	0.18	0.16	0.046	0.043	0.044	0.077	0.1	0.055	0.11	0.7	0.067	0.06	0.053	0.077	-0.016	0.025	0.09	0.12	0.064	-0.016	0.028	0.052	0.16	0.23	0.2	0.13	0.22	0.15	0.21	0.088	0.051	0.11	0.074	0.16	-0.014	-0.031	0.12	0.13	0.015	0.043	0.27	0.088	0.21	-0.057	-0.046	-0.085	-0.12	-0.15	-0.049	-0.15	-0.063	-0.0085	-0.079	-0.087	-0.089	-0.13	-0.15	-0.1	-0.073	-0.066	-0.036	-0.15	-0.16	-0.17	-0.075	-0.15	-0.15	-0.18	-0.082	0.065	-0.082	-0.16	-0.098	-0.15	-0.09	-0.05	-0.14	-0.13	-0.082	-0.14	-0.096	-0.15	-0.097	-0.19	-0.095	-0.14	-0.06	-0.12	-0.14
OVCAR3_B1_102_chr11_rank_deviation_in_rank	-0.074	-0.082	0.028	-0.19	-0.023	0.19	0.084	0.11	-0.2	0.05	0.046	0.061	0.18	0.25	0.0035	1	0.3	0.25	0.099	0.0027	-0.039	-0.0035	0.056	-0.024	-0.043	0.17	0.056	0.032	-0.021	0.19	0.18	0.2	0.11	0.2	-0.064	0.1	0.19	0.14	0.14	-0.04	0.027	0.19	0.14	0.055	-0.043	-0.055	0.14	-0.006	-0.054	-0.2	-0.11	-0.21	-0.17	-0.078	-0.15	-0.015	-0.11	-0.15	-0.073	-0.1	-0.08	-0.16	-0.048	-0.18	-0.23	-0.15	-0.22	-0.11	-0.084	-0.068	0.054	-0.12	0.057	0.16	-0.1	-0.13	-0.16	0.1	-0.034	-0.06	0.018	-0.085	-0.11	0.064	-0.052	-0.023	0.028	0.15	-0.12	0.11	-0.093	0.076	-0.01	0.14	0.1	0.087	0.15	0.032	0.031	0.14	0.094	0.14	0.036	0.092	0.11	0.13	0.23	0.23	0.045	0.81	0.3	0.2	0.086	0.00096	0.1	0.15	0.12	0.097	0.086	0.079	0.066	0.036	0.075	0.17	0.099	0.15	0.058	0.18	-0.021	0.16	0.21	0.11	0.055	0.026	0.049	0.17	0.079	0.11	0.04	-0.032	0.088	0.05	-0.11	-0.18	-0.16	-0.14	-0.11	-0.083	-0.14	-0.014	-0.09	-0.11	-0.11	-0.086	-0.14	-0.19	-0.13	-0.15	-0.19	-0.15	-0.24	-0.12	-0.16	-0.14	-0.05	-0.1	-0.085	0.018	-0.14	-0.18	-0.17	-0.042	-0.097	-0.036	-0.086	-0.17	-0.18	-0.1	-0.12	-0.12	-0.11	-0.032	-0.16	-0.029	-0.15	-0.038	-0.087	-0.01
OVCAR3_B2_73_chr4_rank_deviation_in_rank	-0.096	-0.0021	-0.048	-0.2	0.19	0.36	0.26	0.18	-0.22	0.17	0.15	0.22	0.23	0.32	0.021	0.3	1	0.27	0.17	0.058	-0.09	-0.063	0.17	-0.093	-0.14	0.35	0.059	0.098	-0.00098	0.3	0.32	0.31	0.25	0.29	-0.0057	0.019	0.24	0.34	0.32	0.0056	0.026	0.35	0.26	0.06	-0.063	-0.016	0.35	-0.03	-0.096	-0.28	-0.18	-0.33	-0.36	-0.15	-0.28	-0.18	-0.17	-0.32	-0.18	-0.2	-0.13	-0.24	-0.039	-0.29	-0.42	-0.29	-0.28	-0.22	-0.082	-0.092	-0.006	-0.27	0.024	0.13	-0.033	-0.16	-0.19	0.11	-0.13	-0.086	0.013	-0.14	-0.16	0.081	-0.14	-0.032	-0.021	0.12	-0.19	0.047	-0.2	-0.081	-0.084	0.1	0.2	0.23	0.19	0.13	0.22	0.25	0.24	0.2	0.12	0.22	0.22	0.28	0.3	0.28	0.17	0.33	0.87	0.31	0.21	0.15	0.26	0.18	0.23	0.22	0.19	0.28	0.17	0.19	0.087	0.24	0.25	0.22	0.23	0.22	0.14	0.24	0.29	0.25	0.22	0.18	0.18	0.29	0.24	0.23	0.17	0.019	0.27	0.064	-0.21	-0.25	-0.27	-0.26	-0.27	-0.17	-0.29	-0.17	-0.19	-0.27	-0.24	-0.21	-0.27	-0.32	-0.22	-0.22	-0.38	-0.31	-0.33	-0.26	-0.2	-0.24	-0.2	-0.27	-0.18	-0.11	-0.18	-0.26	-0.24	-0.13	-0.26	-0.11	-0.18	-0.28	-0.29	-0.2	-0.26	-0.25	-0.22	-0.16	-0.25	-0.15	-0.31	-0.23	-0.23	-0.14
OVCAR3_B3_78_chr6_rank_deviation_in_rank	-0.047	-0.036	0.082	-0.28	0.064	0.22	0.18	0.12	-0.15	0.036	0.032	0.057	0.25	0.39	0.013	0.25	0.27	1	0.14	-0.055	-0.016	0.019	0.072	-0.036	-0.044	0.16	0.036	0.05	0.086	0.28	0.26	0.3	0.14	0.28	-0.11	0.062	0.25	0.25	0.19	-0.19	-0.099	0.28	0.22	0.051	-0.17	-0.02	0.25	-0.0057	-0.05	-0.29	-0.19	-0.24	-0.3	-0.042	-0.28	-0.092	0.017	-0.18	-0.096	-0.15	-0.12	-0.24	-0.084	-0.16	-0.32	-0.14	-0.26	-0.098	-0.19	-0.051	0.011	-0.12	-0.044	0.079	-0.014	-0.21	-0.3	0.067	-0.14	-0.091	0.043	-0.19	-0.18	0.13	-0.079	0.026	0.032	0.2	-0.19	0.15	-0.023	0.029	-0.091	0.11	0.12	0.1	0.17	-0.024	0.088	0.12	0.16	0.12	0.057	0.068	0.083	0.099	0.25	0.33	0.11	0.25	0.24	0.78	0.16	0.041	0.19	0.14	0.12	0.17	0.15	0.14	0.12	0.12	0.11	0.22	0.22	0.21	0.13	0.21	0.034	0.19	0.24	0.2	0.15	-0.022	0.049	0.21	0.2	0.16	0.024	0.013	0.18	0.059	-0.11	-0.27	-0.22	-0.19	-0.25	-0.095	-0.27	-0.094	-0.032	-0.16	-0.14	-0.14	-0.21	-0.28	-0.18	-0.11	-0.27	-0.13	-0.28	-0.16	-0.24	-0.14	-0.12	-0.1	-0.17	-0.084	-0.12	-0.26	-0.32	-0.1	-0.2	-0.1	-0.1	-0.26	-0.26	-0.084	-0.16	-0.11	-0.12	-0.028	-0.21	-0.0059	-0.12	-0.082	-0.17	-0.053
OVCAR3_B4_24_chr11_rank_deviation_in_rank	0.019	-0.071	0.038	-0.12	0.038	0.23	0.12	0.086	-0.0018	0.08	0.16	0.078	0.049	0.16	0.072	0.099	0.17	0.14	1	0.098	-0.093	-0.074	-0.0018	-0.21	-0.12	0.18	-0.022	0.098	0.23	0.31	0.19	0.23	0.14	0.25	0.1	-0.053	0.14	0.25	0.059	-0.058	-0.16	0.2	0.18	-0.12	-0.24	0.15	0.2	0.1	-0.014	-0.11	-0.08	-0.12	-0.22	-0.038	-0.17	-0.18	-0.042	-0.17	0.00071	-0.1	-0.15	-0.068	0.0057	-0.17	-0.13	-0.042	-0.16	-0.12	-0.16	-0.13	-0.055	-0.21	-0.018	-0.015	0.038	-0.086	-0.2	-0.032	-0.097	-0.14	-0.045	-0.061	-0.088	0.088	-0.023	0.037	-0.043	0.03	-0.16	-0.089	-0.054	-0.018	-0.088	0.029	0.17	0.08	0.15	0.063	0.042	0.13	0.094	0.1	0.15	0.089	0.16	0.098	0.12	0.14	0.15	0.14	0.14	0.18	0.72	0.14	0.16	0.087	0.056	0.047	0.11	0.17	0.088	0.15	0.21	0.25	0.18	0.14	0.13	0.17	0.16	0.12	0.15	0.18	0.036	0.089	0.026	0.14	0.18	0.076	-0.014	0.15	0.13	0.13	-0.094	-0.11	-0.16	-0.063	-0.15	-0.048	-0.19	-0.17	-0.093	-0.15	-0.06	-0.11	-0.22	-0.14	-0.14	-0.11	-0.087	-0.053	-0.19	-0.16	-0.2	-0.2	-0.17	-0.19	-0.18	-0.15	-0.068	-0.16	-0.24	-0.17	-0.17	-0.14	-0.16	-0.18	-0.19	-0.13	-0.12	-0.13	-0.17	-0.13	-0.19	-0.18	-0.14	-0.12	-0.19	-0.12
OVCAR3_B5_10_chr20_rank_deviation_in_rank	-0.0018	-0.052	0.11	0.16	0.22	0.19	0.11	0.041	0.14	0.18	0.17	0.21	-0.096	-0.045	-0.045	0.0027	0.058	-0.055	0.098	1	-0.14	-0.06	0.082	-0.22	-0.28	0.18	0.0088	0.027	0.18	0.2	-0.034	0.087	0.12	0.051	0.22	-0.27	0.089	0.22	-0.086	0.2	-0.099	0.065	-0.047	-0.33	-0.15	0.22	0.17	0.062	-0.088	0.15	0.13	-0.1	-0.078	-0.15	0.085	-0.24	-0.17	-0.13	-0.067	-0.08	-0.0059	0.18	0.012	-0.25	0.0093	-0.11	-0.047	-0.09	-0.072	-0.15	-0.063	-0.22	0.08	-0.12	-0.028	0.16	0.14	-0.09	-0.018	-0.2	-0.16	0.11	-0.0085	0.025	0.1	0.071	-0.042	-0.21	-0.016	-0.27	-0.0067	-0.055	0.033	-0.073	0.14	0.076	0.18	0.25	0.21	0.11	0.081	0.069	0.2	0.18	0.19	0.21	0.014	-0.029	0.068	0.075	0.087	0.056	0.14	0.7	0.099	0.065	0.11	0.03	-0.03	0.17	0.095	0.088	0.17	0.16	0.012	0.036	0.14	0.0066	0.24	-0.029	0.11	0.16	-0.063	0.24	0.042	0.017	0.0093	-0.096	0.026	0.21	0.11	0.087	-0.17	0.12	-0.0012	-0.081	-0.055	-0.18	0.034	-0.25	-0.19	-0.12	-0.13	-0.12	-0.11	0.065	-0.13	-0.25	0.015	-0.14	-0.11	-0.15	-0.16	-0.2	-0.16	-0.23	-0.074	-0.22	-0.11	0.033	0.023	-0.2	-0.11	-0.2	-0.24	-0.044	-0.12	-0.14	-0.033	-0.1	-0.17	-0.29	-0.088	-0.3	-0.11	-0.16	-0.079	-0.18
OVCAR3_B6_41_chr4_rank_deviation_in_rank	0.085	0.13	0.067	-0.031	-0.17	-0.36	-0.25	-0.14	0.005	-0.12	-0.16	-0.069	0.046	-0.079	-0.14	-0.039	-0.09	-0.016	-0.093	-0.14	1	0.056	-0.13	0.21	0.14	-0.19	-0.11	-0.14	-0.25	-0.28	-0.17	-0.25	-0.24	-0.24	-0.22	0.014	-0.074	-0.18	-0.21	-0.11	-0.011	-0.2	-0.28	0.073	0.046	-0.3	-0.23	-0.17	0.14	0.12	0.14	0.2	0.26	0.18	0.13	0.2	0.12	0.27	0.067	0.22	0.16	0.11	-0.022	0.25	0.16	0.11	0.086	0.22	0.16	0.1	0.063	0.28	0.022	0.071	-0.0073	0.092	0.068	-0.0045	0.19	0.16	0.094	0.018	0.16	0.015	0.12	0.057	0.095	0.1	0.19	0.22	0.18	0.1	0.21	0.12	-0.15	-0.078	-0.11	-0.21	-0.19	-0.27	-0.22	-0.16	-0.18	-0.15	-0.2	-0.13	-0.11	-0.1	-0.23	-0.16	-0.12	-0.13	-0.18	-0.19	0.36	-0.16	-0.17	-0.11	-0.14	-0.16	-0.19	-0.23	-0.26	-0.23	-0.18	-0.17	-0.21	-0.18	-0.25	-0.18	-0.12	-0.11	-0.18	-0.22	-0.16	-0.16	-0.27	-0.14	-0.16	-0.3	-0.15	-0.22	0.19	0.12	0.21	0.12	0.18	0.18	0.13	0.15	0.13	0.21	0.12	0.19	0.25	0.18	0.16	0.17	0.09	0.13	0.11	0.24	0.23	0.17	0.16	0.21	0.17	0.2	0.088	0.16	0.12	0.14	0.24	0.15	0.19	0.16	0.24	0.19	0.2	0.22	0.21	0.23	0.21	0.28	0.23	0.21	0.24	0.21
OVCAR3_B7_99_chr5_rank_deviation_in_rank	-0.039	-0.0032	0.00049	-0.09	-0.13	-0.12	-0.062	-0.051	-0.031	-0.11	-0.14	-0.15	0.031	0.041	-0.011	-0.0035	-0.063	0.019	-0.074	-0.06	0.056	1	-0.073	0.0027	0.16	-0.051	0.05	-0.058	-0.098	-0.0011	-0.079	-0.044	-0.11	-0.034	-0.086	0.11	0.0099	-0.048	-0.025	-0.018	0.0057	-0.099	-0.084	0.14	0.015	-0.09	-0.0095	-0.061	-0.0059	-0.031	0.014	0.056	0.08	-0.017	0.019	0.086	0.0063	0.047	-0.025	0.0028	0.099	-0.01	-0.0031	0.054	0.0089	0.037	0.032	0.11	0.11	0.095	0.084	0.12	0.035	0.037	-0.12	-0.034	-0.018	0.02	0.052	0.028	0.0012	0.015	-0.02	0.047	0.058	0.062	0.11	0.13	0.047	0.15	0.1	0.11	0.11	0.13	-0.083	-0.04	-0.034	-0.1	-0.11	-0.12	-0.06	-0.02	-0.054	-0.11	-0.12	-0.13	0.0017	0.045	-0.075	-0.011	-0.057	-0.05	-0.12	-0.13	-0.075	0.6	-0.074	-0.11	0.015	-0.074	-0.033	-0.11	-0.078	0.02	-0.1	-0.011	-0.12	-0.021	-0.13	-0.018	-0.024	-0.012	-0.053	-0.1	-0.089	-0.073	-0.1	0.023	-0.084	-0.082	0.012	-0.058	0.016	-0.028	0.026	0.045	0.062	-0.03	-0.01	0.061	0.022	0.01	-0.0077	0.0015	0.11	0.0073	0.03	0.02	-0.017	0.031	0.03	0.1	0.089	0.084	0.09	0.099	0.032	0.058	-0.061	-0.028	-0.0064	0.032	0.058	0.035	0.01	0.035	0.00018	0.065	0.05	0.089	0.099	0.13	0.017	0.14	0.091	0.095	0.096	0.12
OVCAR3_B8_EMPTY_VECTOR_no_chro_rank_deviation_in_rank	-0.082	-0.081	0.011	-0.048	0.16	0.29	0.16	0.2	-0.11	0.11	0.1	0.12	0.068	0.15	0.11	0.056	0.17	0.072	-0.0018	0.082	-0.13	-0.073	1	-0.087	-0.16	0.22	0.11	0.043	0.17	0.18	0.16	0.29	0.28	0.21	0.064	-0.026	0.12	0.27	0.26	0.087	0.042	0.22	0.15	0.0037	-0.012	0.094	0.22	0.042	-0.14	-0.23	-0.057	-0.22	-0.23	-0.12	-0.13	-0.16	-0.14	-0.22	-0.13	-0.21	-0.037	-0.088	-0.015	-0.2	-0.23	-0.21	-0.15	-0.15	-0.09	-0.00082	-0.043	-0.21	-0.0066	0.026	-0.036	-0.11	-0.087	0.027	-0.083	-0.072	0.026	-0.09	-0.095	0.071	-0.048	0.04	-0.015	0.041	-0.13	-0.08	-0.13	0.014	-0.052	0.053	0.12	0.076	0.15	0.16	0.16	0.23	0.14	0.2	0.093	0.11	0.15	0.16	0.14	0.13	0.16	0.12	0.17	0.12	0.066	0.11	0.13	0.091	0.85	0.12	0.067	0.16	0.15	0.096	0.18	0.12	0.13	0.19	0.21	0.15	0.13	0.12	0.14	0.19	0.19	0.15	0.12	0.15	0.12	0.13	0.12	0.09	0.14	0.088	-0.2	-0.23	-0.17	-0.18	-0.19	-0.14	-0.17	-0.17	-0.17	-0.2	-0.18	-0.2	-0.15	-0.18	-0.16	-0.15	-0.2	-0.24	-0.2	-0.2	-0.19	-0.12	-0.17	-0.23	-0.17	-0.13	-0.13	-0.19	-0.16	-0.14	-0.17	-0.099	-0.11	-0.21	-0.2	-0.13	-0.15	-0.15	-0.18	-0.16	-0.18	-0.19	-0.22	-0.13	-0.16	-0.11
OVCAR3_B9_91_chr9_rank_deviation_in_rank	0.13	0.094	0.038	0.017	-0.21	-0.33	-0.19	-0.25	-0.033	-0.19	-0.2	-0.095	0.025	-0.15	-0.059	-0.024	-0.093	-0.036	-0.21	-0.22	0.21	0.0027	-0.087	1	0.12	-0.36	-0.22	-0.045	-0.2	-0.29	-0.14	-0.23	-0.18	-0.23	-0.23	0.12	-0.16	-0.29	-0.16	-0.18	-0.0024	-0.29	-0.24	0.16	0.2	-0.27	-0.3	-0.18	0.14	0.16	0.14	0.29	0.24	0.27	0.17	0.3	0.23	0.26	0.11	0.29	0.14	0.036	0.013	0.33	0.17	0.13	0.071	0.18	0.13	0.11	0.033	0.32	0.03	0.16	-0.00063	0.11	0.14	0.12	0.15	0.27	0.13	0.014	0.058	-0.045	0.062	0.053	0.046	0.059	0.22	0.24	0.096	0.14	0.16	0.093	-0.18	-0.15	-0.18	-0.23	-0.23	-0.25	-0.19	-0.26	-0.28	-0.23	-0.27	-0.18	-0.16	-0.18	-0.21	-0.17	-0.14	-0.18	-0.28	-0.25	-0.19	-0.25	-0.18	0.43	-0.2	-0.32	-0.28	-0.18	-0.26	-0.25	-0.16	-0.16	-0.18	-0.19	-0.29	-0.16	-0.22	-0.22	-0.15	-0.3	-0.19	-0.27	-0.26	-0.12	-0.09	-0.29	-0.24	-0.26	0.19	0.13	0.22	0.19	0.13	0.21	0.16	0.25	0.22	0.17	0.15	0.24	0.24	0.13	0.18	0.23	0.083	0.13	0.11	0.22	0.19	0.19	0.16	0.26	0.21	0.31	0.12	0.17	0.2	0.28	0.22	0.24	0.23	0.18	0.17	0.18	0.17	0.23	0.2	0.23	0.25	0.29	0.19	0.24	0.23	0.22
OVCAR3_C10_105_chr18_rank_deviation_in_rank	0.055	0.062	-0.071	-0.12	-0.28	-0.31	-0.18	-0.081	-0.08	-0.23	-0.18	-0.27	0.02	-0.063	-0.16	-0.043	-0.14	-0.044	-0.12	-0.28	0.14	0.16	-0.16	0.12	1	-0.27	-0.096	-0.17	-0.26	-0.25	-0.13	-0.25	-0.34	-0.1	-0.21	0.22	-0.08	-0.25	-0.17	-0.22	-0.024	-0.14	-0.1	0.22	0.037	-0.18	-0.22	0.0022	0.13	0.02	-0.01	0.29	0.24	0.24	0.07	0.35	0.24	0.3	0.18	0.22	0.085	-0.021	0.013	0.39	0.19	0.25	0.2	0.22	0.13	0.25	0.093	0.35	-0.028	0.081	0.058	-0.051	-0.06	0.051	0.079	0.21	0.15	0.017	0.12	0.01	0.043	-0.045	0.17	0.17	0.16	0.28	0.16	0.14	0.1	0.11	-0.19	-0.11	-0.2	-0.27	-0.26	-0.21	-0.17	-0.11	-0.24	-0.24	-0.23	-0.27	-0.1	-0.071	-0.31	-0.15	-0.15	-0.22	-0.27	-0.36	-0.25	-0.094	-0.19	-0.25	0.47	-0.29	-0.27	-0.29	-0.24	-0.19	-0.21	-0.15	-0.35	-0.06	-0.32	-0.12	-0.13	-0.16	-0.22	-0.35	-0.25	-0.11	-0.2	-0.11	-0.24	-0.19	-0.14	-0.085	0.2	0.012	0.095	0.24	0.16	0.21	0.076	0.31	0.27	0.22	0.2	0.2	0.2	0.072	0.19	0.31	0.091	0.22	0.2	0.23	0.21	0.28	0.22	0.28	0.14	0.25	0.19	0.043	0.017	0.21	0.19	0.22	0.22	0.16	0.21	0.21	0.16	0.17	0.26	0.28	0.17	0.32	0.23	0.24	0.19	0.22
OVCAR3_C11_16_chr17_rank_deviation_in_rank	-0.19	-0.0099	0.041	-0.055	0.31	0.5	0.29	0.37	0.042	0.31	0.25	0.21	0.13	0.36	0.016	0.17	0.35	0.16	0.18	0.18	-0.19	-0.051	0.22	-0.36	-0.27	1	0.2	0.046	0.28	0.43	0.25	0.45	0.35	0.32	0.12	-0.054	0.26	0.52	0.27	0.14	-0.0062	0.42	0.27	-0.17	-0.28	0.2	0.51	0.084	-0.24	-0.28	-0.16	-0.42	-0.42	-0.33	-0.28	-0.36	-0.25	-0.43	-0.29	-0.37	-0.14	-0.082	-0.011	-0.5	-0.42	-0.38	-0.31	-0.23	-0.18	-0.22	-0.065	-0.42	0.011	0.033	0.021	-0.19	-0.18	0.0062	-0.11	-0.19	-0.082	-0.017	-0.13	0.12	-0.11	0.0029	-0.063	0.019	-0.34	-0.2	-0.16	-0.098	-0.15	0.015	0.24	0.29	0.31	0.33	0.33	0.33	0.27	0.37	0.38	0.34	0.34	0.31	0.31	0.33	0.26	0.3	0.36	0.31	0.32	0.3	0.35	0.29	0.31	0.18	0.23	0.79	0.34	0.23	0.34	0.33	0.24	0.32	0.33	0.21	0.3	0.31	0.34	0.39	0.22	0.35	0.23	0.33	0.29	0.2	0.12	0.24	0.37	0.21	-0.34	-0.25	-0.32	-0.3	-0.3	-0.33	-0.3	-0.34	-0.29	-0.34	-0.37	-0.35	-0.33	-0.23	-0.28	-0.4	-0.31	-0.37	-0.38	-0.31	-0.32	-0.35	-0.31	-0.38	-0.3	-0.27	-0.18	-0.33	-0.29	-0.32	-0.28	-0.2	-0.3	-0.27	-0.33	-0.27	-0.31	-0.31	-0.34	-0.32	-0.39	-0.36	-0.3	-0.3	-0.33	-0.27
OVCAR3_C12_51_chr21_rank_deviation_in_rank	-0.14	0.05	0.025	0.044	0.22	0.21	0.14	0.15	0.036	0.17	0.075	0.057	0.048	0.11	-0.01	0.056	0.059	0.036	-0.022	0.0088	-0.11	0.05	0.11	-0.22	-0.096	0.2	1	-0.028	0.1	0.16	0.023	0.21	0.081	0.13	0.0082	-0.055	0.12	0.24	0.12	0.095	-0.032	0.2	0.14	-0.0093	-0.072	0.1	0.23	0.091	-0.16	-0.13	-0.056	-0.14	-0.16	-0.15	-0.059	-0.13	-0.059	-0.18	-0.086	-0.21	-0.052	0.027	0.096	-0.26	-0.12	-0.15	-0.062	-0.054	0.007	-0.093	0.037	-0.13	0.0089	0.056	0.094	-0.0035	0.0024	0.00031	0.016	-0.018	-0.089	-0.0077	-0.091	-0.008	-0.0016	0.045	0.02	0.0028	-0.14	-0.12	-0.04	-0.051	-0.092	0.035	0.05	0.15	0.13	0.17	0.2	0.15	0.13	0.14	0.14	0.17	0.11	0.076	0.13	0.1	0.044	0.11	0.069	0.049	0.016	0.0096	0.064	0.16	0.14	-0.036	0.038	0.1	0.72	0.018	0.12	0.13	0.0078	0.16	0.042	0.1	0.031	0.045	0.12	0.18	0.052	0.11	0.018	0.17	0.08	0.084	0.044	0.11	0.17	0.12	-0.21	-0.15	-0.12	-0.097	-0.12	-0.16	-0.096	-0.16	-0.081	-0.15	-0.16	-0.2	-0.13	-0.07	-0.047	-0.24	-0.097	-0.16	-0.1	-0.11	-0.11	-0.18	-0.086	-0.13	-0.13	-0.071	0.014	-0.077	-0.054	-0.15	-0.092	-0.039	-0.19	-0.14	-0.18	-0.15	-0.091	-0.081	-0.12	-0.14	-0.18	-0.2	-0.13	-0.17	-0.18	-0.071
OVCAR3_C1_89__chr12_rank_deviation_in_rank	-0.02	-0.13	-0.044	-0.045	0.16	0.29	0.18	0.016	-0.1	0.089	0.15	0.12	0.084	0.14	0.19	0.032	0.098	0.05	0.098	0.027	-0.14	-0.058	0.043	-0.045	-0.17	0.046	-0.028	1	0.22	0.26	0.24	0.27	0.27	0.29	0.19	-0.057	0.053	0.21	0.19	-0.16	-0.16	0.11	0.19	-0.12	-0.18	0.26	0.18	0.16	0.03	-0.13	-0.026	-0.17	-0.25	-0.068	-0.17	-0.11	0.063	-0.17	-0.098	-0.11	-0.13	-0.19	-0.093	-0.18	-0.095	-0.034	-0.19	-0.16	-0.18	-0.095	-0.1	-0.2	-0.076	0.024	0.11	-0.098	-0.17	-0.021	-0.15	-0.062	-0.025	-0.088	-0.059	0.04	-0.094	0.056	-0.059	-0.057	-0.21	-0.012	-0.11	0.067	-0.13	-0.01	0.17	0.062	0.12	0.17	0.18	0.2	0.15	0.058	0.1	0.13	0.2	0.14	0.18	0.14	0.22	0.11	0.12	0.13	0.13	0.074	0.13	0.13	0.11	0.18	0.082	0.04	0.091	0.76	0.21	0.2	0.19	0.19	0.21	0.24	0.2	0.14	0.11	0.14	0.14	0.015	0.042	0.084	0.17	0.077	0.046	0.24	0.13	0.18	-0.068	-0.14	-0.12	-0.13	-0.18	-0.1	-0.17	-0.1	-0.0034	-0.14	-0.16	-0.12	-0.22	-0.25	-0.2	-0.13	-0.052	-0.069	-0.22	-0.2	-0.23	-0.17	-0.22	-0.16	-0.21	-0.14	-0.021	-0.17	-0.2	-0.15	-0.21	-0.063	-0.16	-0.2	-0.17	-0.15	-0.18	-0.13	-0.17	-0.2	-0.23	-0.14	-0.2	-0.092	-0.21	-0.16
OVCAR3_C2_31_chr12_rank_deviation_in_rank	-0.14	-0.17	0.0029	0.0011	0.2	0.34	0.18	0.095	0.037	0.15	0.1	0.14	-0.097	0.11	0.25	-0.021	-0.00098	0.086	0.23	0.18	-0.25	-0.098	0.17	-0.2	-0.26	0.28	0.1	0.22	1	0.36	0.21	0.31	0.37	0.29	0.28	-0.15	0.15	0.34	0.25	0.0013	-0.16	0.22	0.24	-0.18	-0.14	0.39	0.3	0.22	-0.12	-0.07	-0.042	-0.24	-0.32	-0.2	-0.14	-0.27	-0.12	-0.26	-0.11	-0.22	-0.13	-0.059	0.031	-0.3	-0.13	-0.11	-0.15	-0.16	-0.24	-0.12	-0.071	-0.33	0.0012	-0.054	0.017	-0.074	-0.16	-0.051	-0.2	-0.19	-0.039	-0.018	-0.097	0.055	-0.079	0.042	-0.13	-0.16	-0.25	-0.2	-0.081	0.023	-0.18	-0.00025	0.076	0.03	0.14	0.17	0.2	0.24	0.16	0.1	0.18	0.14	0.14	0.17	0.048	0.091	0.3	0.095	0.033	0.18	0.26	0.22	0.096	0.093	0.19	0.1	0.046	0.25	0.19	0.25	0.84	0.29	0.19	0.22	0.32	0.22	0.31	0.1	0.16	0.26	0.21	0.16	0.04	0.15	0.23	0.061	0.089	0.37	0.21	0.25	-0.18	-0.072	-0.13	-0.19	-0.27	-0.19	-0.16	-0.25	-0.16	-0.23	-0.16	-0.22	-0.23	-0.14	-0.14	-0.26	-0.087	-0.12	-0.18	-0.2	-0.29	-0.2	-0.19	-0.31	-0.17	-0.21	-0.1	-0.16	-0.22	-0.21	-0.27	-0.2	-0.16	-0.16	-0.19	-0.16	-0.18	-0.15	-0.26	-0.28	-0.26	-0.29	-0.16	-0.12	-0.24	-0.16
OVCAR3_C3_54_chr3_rank_deviation_in_rank	-0.18	-0.2	-0.00075	-0.2	0.27	0.57	0.36	0.25	-0.11	0.21	0.23	0.18	0.12	0.43	0.25	0.19	0.3	0.28	0.31	0.2	-0.28	-0.0011	0.18	-0.29	-0.25	0.43	0.16	0.26	0.36	1	0.43	0.58	0.44	0.56	0.26	-0.084	0.33	0.53	0.42	-0.073	-0.14	0.47	0.41	-0.13	-0.31	0.42	0.49	0.34	-0.18	-0.35	-0.16	-0.46	-0.57	-0.28	-0.41	-0.36	-0.27	-0.45	-0.19	-0.37	-0.25	-0.25	-0.049	-0.47	-0.41	-0.29	-0.33	-0.28	-0.29	-0.25	-0.16	-0.46	-0.056	0.052	0.014	-0.23	-0.34	-0.0085	-0.27	-0.37	-0.086	-0.12	-0.26	0.08	-0.17	0.0066	-0.081	0.018	-0.44	-0.089	-0.21	0.0055	-0.15	0.011	0.3	0.24	0.35	0.28	0.34	0.41	0.34	0.32	0.33	0.31	0.35	0.32	0.36	0.43	0.34	0.36	0.37	0.36	0.35	0.25	0.27	0.38	0.31	0.21	0.22	0.31	0.28	0.32	0.44	0.9	0.33	0.45	0.34	0.45	0.32	0.26	0.4	0.38	0.28	0.17	0.13	0.4	0.34	0.21	0.085	0.43	0.36	0.43	-0.3	-0.32	-0.31	-0.3	-0.41	-0.28	-0.41	-0.31	-0.26	-0.34	-0.28	-0.34	-0.42	-0.37	-0.31	-0.33	-0.31	-0.31	-0.38	-0.33	-0.42	-0.39	-0.38	-0.39	-0.37	-0.26	-0.17	-0.37	-0.42	-0.32	-0.39	-0.33	-0.32	-0.38	-0.44	-0.32	-0.35	-0.32	-0.38	-0.33	-0.49	-0.3	-0.38	-0.27	-0.33	-0.28
OVCAR3_C4_22_chr5_rank_deviation_in_rank	-0.11	-0.059	-0.044	-0.23	0.14	0.41	0.31	0.28	-0.17	0.12	0.15	0.15	0.17	0.39	0.18	0.18	0.32	0.26	0.19	-0.034	-0.17	-0.079	0.16	-0.14	-0.13	0.25	0.023	0.24	0.21	0.43	1	0.47	0.33	0.48	0.13	0.00028	0.19	0.31	0.34	-0.22	-0.17	0.39	0.33	-0.033	-0.19	0.26	0.34	0.22	-0.046	-0.36	-0.21	-0.32	-0.46	-0.066	-0.41	-0.19	-0.096	-0.32	-0.12	-0.26	-0.23	-0.31	-0.085	-0.24	-0.38	-0.16	-0.24	-0.24	-0.28	-0.13	-0.067	-0.28	-0.13	0.1	0.073	-0.27	-0.34	0.055	-0.24	-0.17	0.076	-0.18	-0.16	0.12	-0.24	-0.024	-0.062	0.14	-0.37	0.063	-0.19	-0.055	-0.22	0.017	0.23	0.21	0.21	0.14	0.18	0.28	0.28	0.29	0.17	0.19	0.23	0.22	0.28	0.34	0.29	0.26	0.31	0.32	0.27	0.11	0.25	0.21	0.25	0.23	0.22	0.21	0.18	0.29	0.25	0.35	0.81	0.36	0.28	0.36	0.23	0.26	0.25	0.2	0.25	0.056	0.08	0.32	0.29	0.22	0.12	0.28	0.23	0.28	-0.17	-0.34	-0.3	-0.21	-0.33	-0.1	-0.41	-0.16	-0.15	-0.24	-0.21	-0.24	-0.34	-0.38	-0.28	-0.13	-0.31	-0.17	-0.29	-0.29	-0.36	-0.24	-0.25	-0.23	-0.32	-0.15	-0.1	-0.34	-0.38	-0.2	-0.34	-0.18	-0.14	-0.33	-0.3	-0.21	-0.34	-0.26	-0.28	-0.16	-0.37	-0.15	-0.29	-0.24	-0.32	-0.2
OVCAR3_C5_33_chr17_rank_deviation_in_rank	-0.18	-0.15	0.019	-0.18	0.3	0.57	0.4	0.3	-0.16	0.22	0.2	0.17	0.2	0.44	0.26	0.2	0.31	0.3	0.23	0.087	-0.25	-0.044	0.29	-0.23	-0.25	0.45	0.21	0.27	0.31	0.58	0.47	1	0.47	0.56	0.18	-0.035	0.34	0.52	0.47	-0.052	-0.11	0.5	0.42	-0.067	-0.22	0.35	0.51	0.26	-0.15	-0.4	-0.2	-0.53	-0.58	-0.25	-0.45	-0.32	-0.26	-0.48	-0.24	-0.36	-0.23	-0.31	-0.054	-0.45	-0.5	-0.3	-0.35	-0.31	-0.24	-0.22	-0.1	-0.45	-0.066	0.1	0.025	-0.28	-0.33	0.022	-0.29	-0.27	-0.052	-0.13	-0.21	0.13	-0.21	-0.025	-0.045	0.073	-0.4	-0.051	-0.23	-0.056	-0.23	0.025	0.3	0.26	0.36	0.3	0.36	0.4	0.36	0.35	0.3	0.3	0.32	0.29	0.41	0.42	0.38	0.35	0.35	0.37	0.31	0.2	0.33	0.36	0.39	0.28	0.25	0.33	0.33	0.35	0.39	0.47	0.37	0.87	0.37	0.44	0.3	0.32	0.4	0.36	0.32	0.2	0.17	0.41	0.36	0.27	0.17	0.36	0.35	0.36	-0.3	-0.37	-0.36	-0.38	-0.44	-0.26	-0.47	-0.29	-0.28	-0.38	-0.35	-0.34	-0.42	-0.44	-0.34	-0.32	-0.4	-0.33	-0.43	-0.37	-0.4	-0.39	-0.37	-0.4	-0.4	-0.27	-0.18	-0.43	-0.43	-0.33	-0.45	-0.25	-0.33	-0.41	-0.42	-0.31	-0.41	-0.37	-0.37	-0.31	-0.47	-0.3	-0.39	-0.33	-0.41	-0.3
OVCAR3_C6_26_chr17_rank_deviation_in_rank	-0.12	-0.087	0.068	0.017	0.32	0.51	0.37	0.25	-0.029	0.24	0.19	0.29	0.066	0.27	0.2	0.11	0.25	0.14	0.14	0.12	-0.24	-0.11	0.28	-0.18	-0.34	0.35	0.081	0.27	0.37	0.44	0.33	0.47	1	0.37	0.23	-0.15	0.16	0.41	0.37	0.043	-0.091	0.3	0.3	-0.18	-0.16	0.25	0.36	0.075	-0.16	-0.25	-0.11	-0.37	-0.44	-0.28	-0.27	-0.34	-0.12	-0.44	-0.26	-0.37	-0.13	-0.14	-0.049	-0.44	-0.37	-0.32	-0.28	-0.2	-0.23	-0.17	-0.13	-0.46	-0.037	0.061	0.15	-0.1	-0.17	-0.0014	-0.15	-0.11	-0.035	-0.1	-0.2	0.087	-0.14	0.031	-0.12	-0.027	-0.38	-0.13	-0.22	-0.074	-0.2	0.01	0.22	0.18	0.31	0.32	0.32	0.37	0.33	0.27	0.26	0.27	0.27	0.33	0.21	0.24	0.34	0.24	0.27	0.29	0.27	0.26	0.27	0.2	0.32	0.26	0.13	0.32	0.26	0.35	0.35	0.32	0.31	0.32	0.84	0.25	0.36	0.22	0.23	0.28	0.31	0.28	0.18	0.22	0.3	0.17	0.18	0.26	0.23	0.17	-0.29	-0.25	-0.24	-0.29	-0.33	-0.29	-0.27	-0.32	-0.2	-0.38	-0.34	-0.37	-0.29	-0.26	-0.3	-0.36	-0.28	-0.33	-0.33	-0.26	-0.34	-0.29	-0.32	-0.42	-0.28	-0.22	-0.058	-0.24	-0.26	-0.26	-0.29	-0.15	-0.24	-0.29	-0.36	-0.25	-0.31	-0.27	-0.33	-0.3	-0.41	-0.29	-0.34	-0.28	-0.33	-0.25
OVCAR3_C7_77_chr1_rank_deviation_in_rank	-0.16	-0.14	-0.097	-0.32	0.17	0.5	0.32	0.24	-0.23	0.11	0.17	0.074	0.17	0.49	0.25	0.2	0.29	0.28	0.25	0.051	-0.24	-0.034	0.21	-0.23	-0.1	0.32	0.13	0.29	0.29	0.56	0.48	0.56	0.37	1	0.2	0.068	0.32	0.44	0.43	-0.23	-0.14	0.45	0.43	-0.054	-0.3	0.41	0.42	0.38	-0.094	-0.43	-0.22	-0.41	-0.54	-0.17	-0.43	-0.22	-0.15	-0.39	-0.13	-0.27	-0.25	-0.35	-0.095	-0.3	-0.39	-0.14	-0.3	-0.27	-0.32	-0.13	-0.054	-0.32	-0.15	0.099	-0.019	-0.31	-0.44	-0.041	-0.34	-0.32	-0.0051	-0.17	-0.23	0.075	-0.19	-0.068	-0.064	0.14	-0.39	0.056	-0.19	0.012	-0.21	0.031	0.26	0.24	0.25	0.16	0.25	0.37	0.29	0.31	0.22	0.22	0.3	0.21	0.36	0.46	0.29	0.35	0.36	0.31	0.25	0.1	0.21	0.31	0.34	0.16	0.26	0.19	0.2	0.3	0.38	0.48	0.33	0.45	0.26	0.89	0.23	0.29	0.38	0.32	0.26	0.018	0.072	0.38	0.31	0.2	0.029	0.42	0.31	0.44	-0.23	-0.4	-0.34	-0.27	-0.4	-0.18	-0.41	-0.2	-0.14	-0.3	-0.22	-0.24	-0.39	-0.43	-0.31	-0.17	-0.32	-0.17	-0.36	-0.32	-0.42	-0.28	-0.27	-0.27	-0.4	-0.19	-0.15	-0.42	-0.49	-0.31	-0.42	-0.27	-0.25	-0.38	-0.39	-0.28	-0.34	-0.34	-0.33	-0.21	-0.45	-0.18	-0.33	-0.24	-0.34	-0.24
OVCAR3_C8_FOXL2NB_GENE_chr3_rank_deviation_in_rank	-0.08	-0.069	-0.016	0.1	0.2	0.26	0.23	0.04	0.071	0.086	0.13	0.094	-0.1	0.016	0.09	-0.064	-0.0057	-0.11	0.1	0.22	-0.22	-0.086	0.064	-0.23	-0.21	0.12	0.0082	0.19	0.28	0.26	0.13	0.18	0.23	0.2	1	-0.14	0.047	0.14	0.038	-0.024	-0.14	0.14	0.13	-0.24	-0.19	0.37	0.15	0.24	-0.014	0.062	0.0099	-0.085	-0.18	-0.09	-0.057	-0.22	-0.063	-0.11	0.015	-0.12	-0.088	0.0027	0.025	-0.21	-0.0054	0.024	-0.0037	-0.14	-0.097	-0.082	-0.1	-0.19	-0.085	-0.067	0.13	0.0021	-0.0075	-0.097	-0.092	-0.14	-0.057	0.046	-0.0082	0.032	-0.079	0.029	-0.14	-0.15	-0.16	-0.16	-0.068	-0.047	-0.16	-0.078	0.077	0.058	0.086	0.19	0.2	0.23	0.2	0.063	0.15	0.12	0.15	0.12	0.018	0.036	0.13	0.021	0.038	-0.004	0.12	0.18	0.028	0.048	0.1	-0.013	-0.015	0.11	0.069	0.17	0.26	0.22	0.097	0.15	0.2	0.13	0.7	0.015	0.067	0.085	0.02	0.061	-0.00075	0.11	0.12	-0.066	-0.033	0.33	0.11	0.23	-0.092	0.037	-0.077	-0.07	-0.15	-0.12	-0.089	-0.22	-0.1	-0.11	-0.048	-0.14	-0.15	-0.064	-0.091	-0.19	-0.0072	-0.0058	-0.053	-0.17	-0.13	-0.12	-0.17	-0.19	-0.19	-0.15	0.034	-0.075	-0.081	-0.18	-0.15	-0.16	-0.14	-0.071	-0.11	-0.12	-0.15	-0.11	-0.21	-0.22	-0.18	-0.2	-0.14	-0.14	-0.19	-0.17
OVCAR3_C9_2_chr6_rank_deviation_in_rank	-0.0018	0.087	-0.061	-0.18	-0.14	-0.053	-0.1	-0.0093	-0.055	-0.078	-0.033	-0.13	0.15	0.11	-0.082	0.1	0.019	0.062	-0.053	-0.27	0.014	0.11	-0.026	0.12	0.22	-0.054	-0.055	-0.057	-0.15	-0.084	0.00028	-0.035	-0.15	0.068	-0.14	1	-0.045	-0.091	-0.029	-0.16	-0.046	-0.007	-0.011	0.12	-0.0067	-0.13	-0.083	-0.08	0.087	-0.12	-0.047	0.096	0.023	0.13	-0.042	0.17	0.2	0.13	0.075	0.065	-0.0098	-0.081	0.032	0.12	0.0092	0.099	0.011	0.11	0.022	0.054	0.056	0.15	-0.043	0.18	0.07	-0.062	-0.11	0.12	0.0074	0.17	0.16	-0.073	0.076	0.014	-0.016	-0.031	0.091	0.16	-0.014	0.18	0.047	0.061	0.0034	0.1	-0.082	-0.00071	-0.09	-0.19	-0.12	-0.013	-0.1	-0.038	-0.1	-0.087	-0.07	-0.11	0.058	0.071	-0.13	0.017	-0.0088	-0.052	-0.15	-0.25	-0.13	-0.015	-0.047	-0.06	0.031	-0.11	-0.13	-0.12	-0.12	-0.075	-0.078	-0.0052	-0.18	0.072	-0.18	0.58	-0.065	-0.086	-0.085	-0.22	-0.14	-0.00034	-0.075	-0.04	-0.11	-0.13	-0.066	-0.098	0.099	-0.11	0.0035	0.067	-0.0055	0.1	-0.031	0.16	0.2	0.086	0.079	0.067	0.037	-0.038	0.084	0.084	-0.031	0.088	0.014	0.1	0.032	0.055	0.071	0.12	0.017	0.18	0.11	-0.02	-0.064	0.13	0.038	0.15	0.13	-0.012	0.085	0.055	0.02	0.046	0.1	0.16	-0.013	0.14	0.066	0.095	0.031	0.12
OVCAR3_D10_67_chr8_rank_deviation_in_rank	-0.15	-0.11	0.037	-0.23	0.1	0.21	0.13	0.15	-0.19	0.072	0.062	0.0025	0.11	0.32	0.1	0.19	0.24	0.25	0.14	0.089	-0.074	0.0099	0.12	-0.16	-0.08	0.26	0.12	0.053	0.15	0.33	0.19	0.34	0.16	0.32	0.047	-0.045	1	0.35	0.24	-0.077	-0.03	0.33	0.22	1.5e-05	-0.14	0.2	0.31	0.23	-0.12	-0.27	-0.086	-0.35	-0.31	-0.13	-0.3	-0.18	-0.17	-0.25	-0.11	-0.25	-0.15	-0.18	-0.072	-0.22	-0.28	-0.16	-0.2	-0.16	-0.17	-0.02	-0.032	-0.2	-0.024	0.11	-0.084	-0.17	-0.22	-0.0066	-0.19	-0.18	0.0058	-0.12	-0.13	0.082	-0.078	0.0014	-0.0076	0.06	-0.21	0.0073	-0.1	0.048	-0.096	0.14	0.15	0.17	0.24	0.1	0.18	0.13	0.15	0.21	0.13	0.17	0.18	0.13	0.25	0.32	0.13	0.27	0.3	0.22	0.12	0.075	0.18	0.23	0.22	0.087	0.15	0.14	0.13	0.08	0.23	0.29	0.11	0.28	0.1	0.28	0.059	0.12	0.89	0.27	0.11	0.027	0.056	0.28	0.13	0.13	0.024	0.22	0.25	0.29	-0.19	-0.25	-0.2	-0.23	-0.2	-0.13	-0.29	-0.16	-0.13	-0.19	-0.16	-0.23	-0.25	-0.24	-0.21	-0.14	-0.21	-0.19	-0.26	-0.18	-0.26	-0.15	-0.19	-0.17	-0.22	-0.088	-0.15	-0.25	-0.27	-0.19	-0.26	-0.12	-0.17	-0.26	-0.24	-0.17	-0.19	-0.18	-0.2	-0.17	-0.28	-0.15	-0.23	-0.12	-0.22	-0.072
OVCAR3_D11_SCRAMBLE_2_no_chro_rank_deviation_in_rank	-0.23	-0.093	0.042	-0.11	0.37	0.53	0.34	0.35	-0.053	0.3	0.25	0.22	0.13	0.41	0.12	0.14	0.34	0.25	0.25	0.22	-0.18	-0.048	0.27	-0.29	-0.25	0.52	0.24	0.21	0.34	0.53	0.31	0.52	0.41	0.44	0.14	-0.091	0.35	1	0.4	0.081	-0.053	0.5	0.38	-0.1	-0.21	0.27	0.57	0.22	-0.21	-0.35	-0.19	-0.52	-0.55	-0.35	-0.35	-0.41	-0.3	-0.5	-0.26	-0.44	-0.17	-0.17	-0.055	-0.53	-0.47	-0.36	-0.33	-0.32	-0.21	-0.18	-0.095	-0.5	-0.0075	0.012	0.011	-0.2	-0.23	0.025	-0.22	-0.25	-0.053	-0.11	-0.19	0.081	-0.17	-0.0073	-0.032	0.098	-0.37	-0.15	-0.2	-0.1	-0.16	-0.012	0.24	0.28	0.35	0.32	0.4	0.36	0.3	0.37	0.34	0.35	0.35	0.3	0.34	0.39	0.29	0.31	0.38	0.34	0.33	0.27	0.33	0.32	0.37	0.22	0.23	0.39	0.34	0.32	0.4	0.42	0.25	0.38	0.34	0.32	0.28	0.26	0.4	0.89	0.27	0.29	0.2	0.39	0.34	0.24	0.16	0.29	0.43	0.33	-0.34	-0.34	-0.36	-0.39	-0.43	-0.36	-0.38	-0.39	-0.32	-0.41	-0.34	-0.43	-0.37	-0.32	-0.35	-0.42	-0.39	-0.38	-0.41	-0.39	-0.38	-0.36	-0.34	-0.46	-0.34	-0.31	-0.19	-0.36	-0.35	-0.33	-0.39	-0.25	-0.32	-0.37	-0.4	-0.32	-0.37	-0.34	-0.35	-0.27	-0.45	-0.37	-0.37	-0.35	-0.35	-0.32
OVCAR3_D12_71_chr8_rank_deviation_in_rank	-0.17	-0.084	-0.12	-0.14	0.31	0.49	0.32	0.3	-0.13	0.18	0.18	0.19	0.19	0.38	0.21	0.14	0.32	0.19	0.059	-0.086	-0.21	-0.025	0.26	-0.16	-0.17	0.27	0.12	0.19	0.25	0.42	0.34	0.47	0.37	0.43	0.038	-0.029	0.24	0.4	1	-0.097	-0.051	0.39	0.35	0.028	-0.11	0.26	0.43	0.22	-0.12	-0.39	-0.17	-0.44	-0.47	-0.24	-0.38	-0.24	-0.12	-0.41	-0.21	-0.31	-0.18	-0.3	-0.016	-0.33	-0.42	-0.3	-0.24	-0.24	-0.19	-0.16	-0.1	-0.38	-0.084	0.074	0.074	-0.24	-0.3	0.11	-0.22	-0.095	0.025	-0.13	-0.23	0.028	-0.23	-0.0012	-0.098	0.079	-0.36	0.011	-0.26	-0.086	-0.22	-0.024	0.22	0.23	0.19	0.24	0.34	0.35	0.29	0.31	0.24	0.25	0.28	0.28	0.33	0.36	0.32	0.26	0.32	0.29	0.19	0.089	0.26	0.27	0.32	0.25	0.23	0.22	0.25	0.28	0.3	0.34	0.27	0.34	0.31	0.33	0.19	0.26	0.3	0.28	0.81	0.15	0.17	0.31	0.3	0.29	0.19	0.27	0.31	0.31	-0.24	-0.37	-0.28	-0.33	-0.34	-0.24	-0.37	-0.2	-0.16	-0.31	-0.27	-0.28	-0.33	-0.39	-0.27	-0.21	-0.32	-0.3	-0.29	-0.28	-0.31	-0.29	-0.3	-0.33	-0.32	-0.21	-0.1	-0.33	-0.36	-0.19	-0.34	-0.11	-0.18	-0.33	-0.38	-0.28	-0.35	-0.28	-0.33	-0.23	-0.39	-0.2	-0.37	-0.28	-0.35	-0.27
OVCAR3_D1_5_chr7_rank_deviation_in_rank	-0.082	0.093	0.11	0.32	0.17	0.069	0.027	0.096	0.29	0.28	0.093	0.18	-0.12	-0.18	-0.12	-0.04	0.0056	-0.19	-0.058	0.2	-0.11	-0.018	0.087	-0.18	-0.22	0.14	0.095	-0.16	0.0013	-0.073	-0.22	-0.052	0.043	-0.23	-0.024	-0.16	-0.077	0.081	-0.097	1	0.19	-0.032	-0.13	-0.18	0.066	-0.097	0.081	-0.21	-0.097	0.2	0.11	-0.015	0.099	-0.16	0.2	-0.15	-0.1	-0.11	-0.093	-0.09	0.11	0.27	0.13	-0.18	-0.01	-0.18	0.037	-0.0098	0.17	-0.093	0.025	-0.15	0.16	-0.053	0.087	0.22	0.35	0.026	0.19	0.075	-0.083	0.19	0.071	-0.0084	0.16	0.13	0.015	-0.1	0.093	-0.32	-0.066	-0.16	0.079	-0.11	-0.073	0.045	0.043	0.2	0.094	0.065	0.022	0.055	0.15	0.19	0.05	0.11	-0.098	-0.16	-0.046	-0.038	-0.013	-0.094	-0.0027	0.15	-0.037	-0.031	0.037	-0.1	-0.14	0.12	0.079	-0.073	-0.024	-0.082	-0.14	-0.075	0.063	-0.23	0.012	-0.098	-0.072	0.061	-0.05	0.71	0.13	-0.056	-0.067	-0.11	0.062	-0.092	0.05	-0.17	-0.1	0.15	0.049	-0.055	0.059	-0.15	0.13	-0.19	-0.12	-0.14	-0.11	-0.11	0.049	0.17	0.049	-0.24	-0.035	-0.19	-0.0092	-0.052	0.097	-0.11	-0.016	-0.18	0.084	-0.085	0.032	0.14	0.26	-0.0046	0.091	0.011	-0.089	0.097	0.019	-0.027	0.078	0.054	-0.014	-0.11	0.035	-0.25	-0.073	-0.14	0.032	-0.12

	OVCAR3_A10_87_chr6_rank_deviation_in_rank	OVCAR3_A11_RNF4_ENH_chr4_rank_deviation_in_rank	OVCAR3_A12_55_chr1_rank_deviation_in_rank	OVCAR3_A1_17_chr17_rank_deviation_in_rank	OVCAR3_A2_29_chr19_rank_deviation_in_rank	OVCAR3_A3_60_chr20_rank_deviation_in_rank	OVCAR3_A4_81_chr7_rank_deviation_in_rank	OVCAR3_A5_21_chr20_rank_deviation_in_rank	OVCAR3_A6_39_chr10_rank_deviation_in_rank	OVCAR3_A7_TP53_GENE_chr17_rank_deviation_in_rank	...	OVCAR3_H11_18_chr12_rank_deviation_in_rank	OVCAR3_H12_49_chr10_rank_deviation_in_rank	OVCAR3_H1_RNF4_GENE_chr4_rank_deviation_in_rank	OVCAR3_H2_79_chr3_rank_deviation_in_rank	OVCAR3_H3_43_chr11_rank_deviation_in_rank	OVCAR3_H4_86_chr5_rank_deviation_in_rank	OVCAR3_H5_88_chr4_rank_deviation_in_rank	OVCAR3_H6_63_chr2_rank_deviation_in_rank	OVCAR3_H8_36_chr14_rank_deviation_in_rank	OVCAR3_H9_25_chr22_rank_deviation_in_rank
OVCAR3_A10_87_LFC	0.568222	-0.043736	-0.039007	-0.211956	0.111316	0.315924	0.230257	0.134299	-0.144830	0.070485	...	-0.055595	-0.024311	0.007351	0.098266	-0.151858	-0.033075	-0.081847	0.006083	-0.090896	0.043687
OVCAR3_A11_RNF4_ENH_LFC	-0.079295	0.619926	-0.033212	-0.203385	0.127392	0.310170	0.229401	0.220814	-0.140995	0.144041	...	-0.082685	0.009996	0.015281	0.143892	-0.188227	-0.017938	-0.103413	-0.074521	-0.084708	0.055335
OVCAR3_A12_55_LFC	-0.123064	-0.077304	0.681566	-0.172145	0.167938	0.324292	0.218324	0.181392	-0.094934	0.181294	...	-0.016866	0.014785	0.029319	0.096209	-0.158908	-0.061298	-0.070981	-0.004456	-0.072668	0.041630
OVCAR3_A1_17_LFC	-0.220800	-0.150980	-0.049692	0.572250	0.368455	0.417204	0.254177	0.228181	-0.008231	0.254528	...	-0.041189	0.021003	-0.036014	-0.128859	-0.141821	-0.245027	-0.121668	-0.162493	-0.119574	-0.074298
OVCAR3_A2_29_LFC	-0.212508	-0.130769	-0.079105	0.048726	0.883247	0.481752	0.305777	0.225140	-0.083755	0.240891	...	-0.122272	-0.017735	-0.098293	-0.093987	-0.238895	-0.231801	-0.187768	-0.169172	-0.144621	-0.089563
OVCAR3_A3_60_LFC	-0.157265	-0.096242	-0.093843	-0.023288	0.325902	0.871387	0.346177	0.251275	-0.053263	0.213503	...	-0.205885	-0.059440	-0.062558	-0.049152	-0.332839	-0.214436	-0.218100	-0.150603	-0.228842	-0.099846
OVCAR3_A4_81_LFC	-0.124499	-0.066388	-0.059749	-0.118788	0.236967	0.422267	0.915173	0.186474	-0.059291	0.173162	...	-0.195282	-0.056998	-0.006908	0.032277	-0.236028	-0.066483	-0.118682	-0.106299	-0.182001	-0.079543
OVCAR3_A5_21_LFC	-0.201672	-0.063435	-0.069628	-0.127766	0.211449	0.415388	0.238486	0.883274	-0.171494	0.174914	...	-0.191507	-0.064355	-0.013077	0.073689	-0.232862	-0.063213	-0.196120	-0.104574	-0.134873	-0.053350
OVCAR3_A6_39_LFC	-0.168512	-0.124053	-0.020193	-0.064252	0.222732	0.374030	0.270908	0.169501	0.559001	0.217807	...	-0.032084	-0.045645	-0.046229	-0.027407	-0.200195	-0.226524	-0.098545	-0.100973	-0.103612	-0.030999
OVCAR3_A7_TP53_GENE_LFC	-0.187593	-0.060676	-0.013126	-0.022996	0.264411	0.387403	0.244624	0.236541	-0.028636	0.876912	...	-0.043515	0.039545	-0.062927	-0.058500	-0.185743	-0.164653	-0.142023	-0.121340	-0.099489	0.028466
OVCAR3_A8_50_LFC	-0.132619	-0.127204	-0.108412	-0.052247	0.239200	0.427974	0.276812	0.203448	-0.104112	0.203614	...	-0.119600	-0.060234	-0.029292	-0.015236	-0.199083	-0.179360	-0.148952	-0.132688	-0.153325	-0.007828
OVCAR3_A9_6_LFC	-0.110074	-0.004436	0.017162	-0.059182	0.224163	0.368074	0.234641	0.216067	-0.038117	0.248820	...	-0.038436	0.043054	-0.040678	0.000490	-0.168375	-0.106839	-0.118563	-0.097228	-0.059448	-0.017530
OVCAR3_B10_45_LFC	-0.175486	-0.095124	-0.058872	-0.280672	0.172804	0.354726	0.238367	0.187515	-0.259873	0.138131	...	-0.140085	-0.020659	0.018806	0.143147	-0.253348	0.048741	-0.123805	-0.042043	-0.121327	0.097669
OVCAR3_B11_65_LFC	-0.132219	-0.123924	-0.044303	-0.360152	0.090064	0.378052	0.223079	0.258463	-0.200559	0.075382	...	-0.194440	-0.049909	0.034721	0.173534	-0.295768	0.069886	-0.122735	-0.002066	-0.174054	0.048671
OVCAR3_B12_95_LFC	-0.106475	-0.110130	0.024158	-0.053299	0.279796	0.414199	0.305102	0.213014	-0.072411	0.192312	...	-0.196926	-0.001523	-0.120040	-0.027470	-0.306682	-0.101128	-0.200550	-0.088461	-0.153787	-0.069040
OVCAR3_B1_102_LFC	-0.147655	-0.134110	-0.034235	-0.243087	0.098940	0.361430	0.211079	0.209107	-0.212447	0.108266	...	-0.129055	-0.054299	-0.017161	0.115766	-0.253003	0.003125	-0.177056	-0.008918	-0.105721	0.081205
OVCAR3_B2_73_LFC	-0.119088	-0.053518	-0.078399	-0.235155	0.190595	0.395821	0.267138	0.192299	-0.225673	0.168374	...	-0.132376	-0.070653	-0.013839	0.101112	-0.227856	-0.000772	-0.196989	-0.087523	-0.125261	0.064261
OVCAR3_B3_78_LFC	-0.120896	-0.057301	0.068700	-0.213249	0.213642	0.373946	0.267816	0.233270	-0.106622	0.163573	...	-0.161442	0.014808	-0.053683	0.095042	-0.302227	0.024321	-0.136036	-0.056314	-0.156297	0.037645
OVCAR3_B4_24_LFC	-0.081731	-0.077212	0.047633	-0.100775	0.203312	0.361549	0.240475	0.213647	-0.004936	0.187582	...	-0.150313	0.030635	-0.097711	-0.000055	-0.265158	-0.146395	-0.167704	-0.104926	-0.166768	-0.020497
OVCAR3_B5_10_LFC	-0.074064	-0.055007	0.100404	0.096595	0.309581	0.313261	0.199105	0.176689	0.072908	0.254427	...	-0.044971	0.052761	-0.111744	-0.162715	-0.151495	-0.243480	-0.115515	-0.132512	-0.071188	-0.095388
OVCAR3_B6_41_LFC	-0.135925	-0.019095	0.064122	-0.135194	0.266887	0.335509	0.242177	0.272512	-0.088916	0.227771	...	-0.167656	0.021526	-0.052375	0.055148	-0.248084	-0.011474	-0.132338	-0.101822	-0.100294	0.017932
OVCAR3_B7_99_LFC	-0.192517	-0.134993	-0.052124	-0.230027	0.141945	0.324116	0.233875	0.217847	-0.167757	0.084864	...	-0.135460	-0.025360	0.021730	0.107161	-0.243925	0.000002	-0.102737	-0.053443	-0.098530	0.064975
OVCAR3_B8_EMPTY_VECTOR_LFC	-0.130407	-0.148026	-0.027780	-0.129890	0.194934	0.410699	0.239061	0.243964	-0.150314	0.119021	...	-0.094606	-0.005954	-0.031962	0.032161	-0.228231	-0.099759	-0.193956	-0.040220	-0.118614	0.019305
OVCAR3_B9_91_LFC	-0.093679	-0.021810	0.041571	-0.083124	0.226157	0.282273	0.229555	0.158620	-0.125861	0.146307	...	-0.195197	0.025061	-0.092670	0.033725	-0.181957	0.015946	-0.152140	-0.063816	-0.095725	0.025778
OVCAR3_C10_105_LFC	-0.145363	-0.052530	-0.048192	-0.187386	0.152684	0.275527	0.216521	0.258614	-0.153669	0.110836	...	-0.195439	-0.045112	0.007533	0.103933	-0.208261	0.043824	-0.094081	-0.043678	-0.133806	0.044526
OVCAR3_C11_16_LFC	-0.148548	-0.008379	0.075638	-0.007366	0.297329	0.401312	0.243596	0.303885	0.077306	0.278255	...	-0.130710	0.018426	-0.095343	-0.027177	-0.302935	-0.181269	-0.155455	-0.106603	-0.131950	-0.028112
OVCAR3_C12_51_LFC	-0.163363	0.001518	0.053162	0.012446	0.335864	0.381263	0.265195	0.246898	0.002021	0.262978	...	-0.122483	0.050385	-0.062289	-0.024314	-0.261992	-0.150499	-0.153869	-0.137327	-0.158584	0.000248
OVCAR3_C1_89_LFC	-0.091473	-0.130586	-0.010868	-0.052581	0.296191	0.413333	0.278019	0.168718	-0.094330	0.196285	...	-0.184706	0.036567	-0.100012	-0.066843	-0.302403	-0.099498	-0.212422	-0.051168	-0.176785	-0.050529
OVCAR3_C2_31_LFC	-0.179140	-0.204267	-0.061920	-0.101158	0.213301	0.401302	0.219190	0.135049	-0.055927	0.145765	...	-0.119425	-0.020226	-0.116248	-0.099623	-0.296303	-0.191825	-0.141931	-0.022725	-0.199631	0.004131
OVCAR3_C3_54_LFC	-0.155279	-0.191975	-0.036404	-0.214849	0.168648	0.447439	0.248818	0.176646	-0.111445	0.130614	...	-0.126205	-0.016621	-0.062452	0.018815	-0.375049	-0.072371	-0.195857	0.018259	-0.111952	0.011996
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
OVCAR3_F3_38_LFC	0.083208	-0.056423	-0.020990	0.159379	-0.190611	-0.373055	-0.228743	-0.311253	0.114390	-0.207897	...	0.151356	-0.006365	-0.033249	-0.165321	0.204089	-0.043213	0.132678	0.037852	0.075594	-0.047281
OVCAR3_F4_47_LFC	0.117672	-0.005855	-0.072103	0.033447	-0.287713	-0.396188	-0.262241	-0.241980	0.020638	-0.246140	...	0.095729	-0.018899	0.037375	-0.024368	0.244626	0.143598	0.175440	0.096268	0.111934	-0.013160
OVCAR3_F5_FOX_ENH_LFC	0.140080	0.025881	-0.063382	0.154039	-0.219918	-0.402035	-0.223345	-0.248585	0.143786	-0.191252	...	0.053731	-0.050121	-0.003759	-0.150755	0.247450	0.012254	0.149895	0.021801	0.092530	-0.047777
OVCAR3_F6_56_LFC	0.099853	0.057136	0.024239	-0.013045	-0.310169	-0.432041	-0.299024	-0.216216	0.027287	-0.229650	...	0.082438	-0.018961	-0.042896	0.003065	0.202273	0.090803	0.165000	0.105997	0.120182	0.000232
OVCAR3_F7_106_LFC	0.110237	0.157710	-0.003641	0.155427	-0.205794	-0.426976	-0.281116	-0.185256	0.135625	-0.130867	...	0.100217	-0.001740	0.057208	-0.039455	0.285585	0.048424	0.121258	0.053850	0.157859	0.001276
OVCAR3_F8_9_LFC	0.139968	0.053778	-0.049062	0.037469	-0.310973	-0.423714	-0.258439	-0.167939	0.008454	-0.253013	...	0.133175	-0.033471	0.048970	-0.019233	0.270123	0.101511	0.157999	0.125863	0.153979	-0.040550
OVCAR3_F9_83_LFC	0.092518	0.071180	-0.039962	0.040546	-0.276872	-0.386539	-0.262550	-0.249385	0.081944	-0.169824	...	0.127740	0.009245	0.014636	-0.017956	0.206205	0.108785	0.092348	0.059269	0.145994	0.035933
OVCAR3_G10_8_LFC	0.121010	0.068376	-0.051050	-0.044309	-0.352728	-0.492354	-0.323318	-0.262005	-0.008825	-0.324587	...	0.162631	-0.059600	0.066651	0.051435	0.299798	0.223960	0.222670	0.075344	0.128721	0.073383
OVCAR3_G11_SCRAMBLE_1_LFC	0.157686	0.148998	0.074834	0.176021	-0.222031	-0.420101	-0.322242	-0.242475	0.141741	-0.140777	...	0.212354	-0.007369	0.025554	-0.064543	0.237917	0.024746	0.127682	0.040806	0.184468	-0.018306